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SmartGuard technology is intended for use with compatible integrated continuous glucose monitors (iCGM), compatible Medtronic continuous glucose monitors (CGMs), and alternate controller enabled (ACE) pumps to automatically adjust the delivery of basal insulin and to automatically deliver correction boluses based on sensor glucose values.
SmartGuard technology is intended for the management of Type 1 diabetes mellitus in persons 7 years of age and older requiring insulin.
SmartGuard technology is intended for single patient use and requires a prescription.

Predictive Low Glucose technology is intended for use with compatible integrated continuous glucose monitors (iCGM), compatible Medtronic continuous glucose monitors (CGMs), and alternate controller enabled (ACE) pumps to automatically suspend delivery of insulin when the sensor glucose value falls below or is predicted to fall below predefined threshold values.
Predictive Low Glucose technology is intended for the management of Type 1 diabetes mellitus in persons 7 years of age and older requiring insulin.
Predictive Low Glucose technology is intended for single patient use and requires a prescription.

SmartGuard Technology, also referred to as Advanced Hybrid Closed Loop (AHCL) algorithm, is a software-only device intended for use by people with Type 1 diabetes for ages 7 years or older. It is an interoperable automated glycemic controller (iAGC) that is intended for use with compatible integrated continuous glucose monitors (iCGM), compatible interoperable Medtronic continuous glucose monitors (CGM) and compatible alternate controller enabled (ACE) pumps to automatically adjust the delivery of basal insulin and to automatically deliver correction boluses based on sensor glucose (SG) values.
The AHCL algorithm resides on the compatible ACE pump, which serves as the host device. It is meant to be integrated in a compatible ACE pump and is an embedded part of the ACE pump firmware.
Inputs to the AHCL algorithm (e.g., SG values, user inputs) are received from the ACE pump (host device), and outputs from the AHCL algorithm (e.g., insulin delivery commands) are sent by the algorithm to the ACE pump. As an embedded part of the firmware, the AHCL algorithm does not connect to or receive data from compatible CGMs; instead, sensor glucose (SG) values or other inputs received by the ACE pump from compatible CGMs via Bluetooth Low Energy (BLE) technology are transmitted to the embedded AHCL algorithm.
The AHCL algorithm works in conjunction with the ACE pump and is responsible for controlling insulin delivery when the ACE pump is in Auto Mode. It includes adaptive control algorithms that autonomously and continually adapt to the ever-changing insulin requirements of each individual.
The AHCL algorithm requires specific therapy settings (target setpoint, insulin-to-carb ratios and active insulin time) that need to be established with the help of a health care provider (HCP) before activation. It also requires five (5) consecutive hours of insulin delivery history, a minimum of two (2) days of total daily dose (TDD) of insulin, a valid sensor glucose (SG) and blood glucose (BG) values to start automated insulin delivery.
When activated, the AHCL algorithm adjusts the insulin dose at five-minute intervals based on CGM data. A basal insulin dose (auto basal) is commanded by the AHCL algorithm to manage glucose levels to the user's target setpoint of 100 mg/dL, 110 mg/dL or 120 mg/dL. The user can also set a temporary target of 150 mg/dL for up to 24 hours. In addition, under certain conditions the algorithm can also automatically command correction boluses (auto correction bolus) without user input.
Meal boluses are the responsibility of the user. The AHCL algorithm includes an integrated bolus calculation feature for user-initiated boluses for meals. When the user inputs their carbohydrate intake, the AHCL algorithm automatically calculates a bolus amount based off available glucose information, entered carbohydrate amount and other patient parameters.
The AHCL algorithm contains several layers of "safeguards" (mitigations) to provide protection against over-delivery or under-delivery of insulin to reduce risk of hypoglycemia and hyperglycemia, respectively.
The AHCL algorithm is a software-only device and does not have a user interface (UI). The compatible ACE pump provides a UI to the user to configure the therapy settings and interact with the algorithm. The AHCL-related alerts/alarms are displayed and managed by the pump.

Predictive Low Glucose Technology, also referred to as the Predictive Low Glucose Management (PLGM) algorithm is a software-only device intended for use by people with Type 1 diabetes for ages 7 years or older. It is an interoperable automated glycemic controller (iAGC) that is intended for use with compatible integrated continuous glucose monitors (iCGM), compatible interoperable Medtronic continuous glucose monitors (CGM) and compatible alternate controller enabled (ACE) pumps to automatically suspend delivery of insulin when the sensor glucose value falls below or is predicted to fall below predefined threshold values.
The PLGM algorithm resides on the compatible ACE Pump, which serves as the host device. It is meant to be integrated in a compatible ACE pump and is an embedded part of the ACE pump firmware.
Inputs to PLGM algorithm (e.g., sensor glucose values, user inputs) are received from the ACE pump (host device), and outputs from PLGM algorithm (e.g., suspend/resume commands) are sent by the algorithm to the ACE pump. As an embedded part of the ACE pump firmware, the PLGM algorithm does not connect to or receive data from compatible CGMs; instead, sensor glucose (SG) values or other inputs are received by the ACE pump from compatible CGMs via Bluetooth Low Energy (BLE) technology are transmitted to the embedded PLGM algorithm.
The PLGM algorithm works in conjunction with the ACE pump. When enabled, the PLGM algorithm is able to suspend insulin delivery for a minimum of 30 minutes and for a maximum of 2 hours based on current or predicted sensor glucose values. It will automatically resume insulin delivery when maximum suspend time of 2 hours has elapsed or when underlying conditions resolve. The user is also able to manually resume insulin at any time.
The PLGM algorithm is a software-only device and does not have a user interface (UI). The compatible ACE pump provides the UI to configure therapy settings and interact with the algorithm. The PLGM-related alerts/alarms are displayed and managed by the pump.

The provided FDA 510(k) clearance letter and supporting documentation detail the acceptance criteria and the studies conducted to prove that Medtronic's SmartGuard Technology and Predictive Low Glucose Technology meet these criteria.

It's important to note that the provided text focuses on demonstrating substantial equivalence to a predicate device, as is typical for 510(k) submissions, rather than establishing de novo acceptance criteria for an entirely novel device. The "acceptance criteria" here refer to the performance benchmarks that demonstrate safety and effectiveness comparable to the predicate and compliance with regulatory special controls.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

The acceptance criteria are generally implied by the comparative data presented against the predicate device (Control-IQ Technology) and the compliance with "iAGC Special Controls requirements defined in 21 CFR 862.1356." The clinical study primarily aims to demonstrate non-inferiority or beneficial outcomes in key glycemic metrics compared to baseline (run-in period).

Table of Acceptance Criteria and Reported Device Performance

Given that this is a 510(k) submission showing substantial equivalence, the "acceptance criteria" are largely derived from the performance of the predicate device and clinical guidelines (e.g., ADA guidelines for Time Below Range). While specific numerical thresholds for acceptance are not explicitly listed as "acceptance criteria" in a table format within the provided text, the results presented serve as the evidence that these implicit criteria are met.

For the purpose of this response, I will synthesize the implied performance targets from the "Pivotal Study Observed Results" and "Supplemental Clinical Data" sections and present the device's reported performance against them.

Table 1: Implied Acceptance Criteria (via Predicate Performance/Clinical Guidelines) and Reported Device Performance for SmartGuard Technology (AHCL Algorithm)

Table 2: Implied Acceptance Criteria and Reported Device Performance for Predictive Low Glucose Technology (PLGM Algorithm)

Study Details

1. Sample Sizes and Data Provenance

Test Set (Clinical Studies):

• SmartGuard Technology (AHCL Algorithm) - Pivotal Study:

  • Adults (18-80 years): 110 subjects enrolled (105 completed).
  • Pediatrics (7-17 years): 112 subjects enrolled (107 completed).
  • Total: 222 subjects enrolled (212 completed).
  • Provenance: Multi-center, single-arm study conducted across 25 sites in the U.S. This implies prospective data collection, specifically designed for this regulatory submission. Home-setting study.

• Predictive Low Glucose Technology (PLGM Algorithm) - Clinical Testing:

  • Adults (14-75 years): 71 subjects. In-clinic study.
  • Pediatrics (7-13 years): 105 subjects. In-clinic evaluation.
  • Total: 176 subjects.
  • Provenance: Multi-center, single-arm, in-clinic studies. Location not explicitly stated but part of a US FDA submission, implying US or international sites adhering to FDA standards. Prospective data.

Training Set:

• SmartGuard Technology & Predictive Low Glucose Technology (Virtual Patient Model):
  • Sample Size: Not explicitly stated as a number of "patients" but referred to as "extensive validation of the simulation environment" and "virtual patient (VP) model."
  • Data Provenance: In-silico simulation studies using Medtronic Diabetes' simulation environment. This is synthetic data generated by computational models, validated against real patient data.

2. Number of Experts and Qualifications for Ground Truth (Test Set)

The clinical studies for both SmartGuard and PLGM technologies involved direct measurement of glucose values via CGM and blood samples (YSI for PLGM study, and HbA1c for SmartGuard study). These are objective physiological measures, not subjective interpretations requiring external expert consensus for "ground truth."

• For SmartGuard Technology: Glucose values were measured by the Simplera Sync CGM and HbA1c by laboratory tests. These are considered objective measures of glycemic control.
• For Predictive Low Glucose Technology: Hypoglycemia induction was monitored with frequent sample testing (FST) and frequent blood sampling for glucose measurements (likely laboratory-grade methods like YSI [Yellow Springs Instrument]).
• Expert involvement: While healthcare professionals (investigators, study coordinators, endocrinologists, nurses) were undoubtedly involved in conducting the clinical studies, managing patient care, and interpreting results, their role was not to establish "ground truth" through consensus or adjudication in the sense of image review. The ground truth was physiological measurements.

3. Adjudication Method for the Test Set

Not applicable in the typical sense of subjective clinical assessments (e.g., radiology image interpretation). Ground truth was established by direct physiological measurements (CGM data, HbA1c, YSI/FST blood glucose). The clinical studies were single-arm studies where subject outcomes were measured, not comparative assessments where multiple readers adjudicate on decisions.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, an MRMC comparative effectiveness study was not described. The clinical studies were single-arm (evaluating the device's performance in a standalone setting against a baseline or predefined safety/efficacy metrics) and did not involve human readers interpreting data from the device to make clinical decisions and then comparing human performance with and without AI assistance. Instead, the AI (algorithm) directly controlled insulin delivery and was evaluated based on patient physiological outcomes.

5. Standalone (Algorithm Only) Performance

Yes, the core of the evaluation is the standalone performance of the algorithms (SmartGuard AHCL and PLGM) in managing glucose, as they are "software-only devices" that reside on the ACE pump firmware. The clinical studies directly measured the physiological impact of the algorithm's actions on glucose levels, HbA1c, and safety parameters in a human population.

6. Type of Ground Truth Used

• Clinical Studies (SmartGuard & PLGM): Objective Physiological Measurements

  • Sensor Glucose (SG) values: From compatible CGMs (Simplera Sync CGM, Guardian 4 CGM)
  • HbA1c: Laboratory measurements of glycosylated hemoglobin.
  • Frequent Sample Testing (FST) / Blood Glucose (BG) values: Clinical laboratory measurements (e.g., YSI) to confirm hypoglycemia during PLGM induction.
  • Adverse Events (AEs): Clinically reported and documented events.
  • These are considered the definitive "ground truth" for evaluating glycemic control and safety.

• In-Silico Simulation Studies: Virtual Patient Model Outputs

  • The "ground truth" for these simulations is the metabolic response of the validated virtual patient models. This computational modeling is used to extend the clinical evidence to various parameter settings and demonstrate equivalence to real-world scenarios.

7. Sample Size for the Training Set

The document does not explicitly state a numerical "sample size" for a distinct "training set" of patients in the traditional machine learning sense for the algorithms themselves. The algorithms are likely developed and refined using a combination of:

• Physiological modeling: Based on established understanding of glucose-insulin dynamics.
• Historical clinical data: From previous similar devices or general diabetes patient populations (though not specified in this document for algorithm training).
• Clinical expertise: Incorporated into the algorithm design.
• The "Virtual Patient Model" itself is a form of simulated data that aids in development and testing. The validation of this model is mentioned as "extensive validation" and establishment of "credibility," implying a robust dataset used to verify its accuracy against real patient responses.

It's typical for complex control algorithms like these to be developed iteratively with physiological models and potentially large historical datasets, but a specific "training set" size for a machine learning model isn't detailed.

8. How the Ground Truth for the Training Set was Established

As noted above, a distinct "training set" with ground truth in the conventional sense of labeling data for a machine learning model isn't described. The development of control algorithms often involves:

• Physiological Simulation: The ground truth for this is the accurate metabolic response as modeled mathematically.
• Clinical Expertise & Design Principles: The ground truth is embedded in the scientific and medical principles guiding the algorithm's control logic.
• Validation of Virtual Patient Model: The "equivalency was demonstrated between Real Patients (RPs) and Virtual Patients (VPs) in terms of predetermined characteristics and clinical outcomes." This suggests that real patient data was used to validate and establish the "ground truth" for the virtual patient model itself, ensuring it accurately mirrors human physiology. This validated virtual patient model then serves as a crucial tool for in-silico testing.

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The Dexcom G7 15 Day Continuous Glucose Monitoring (CGM) System (Dexcom G7 15 Day CGM System or G7 15 Day) is a real time, continuous glucose monitoring device indicated for the management of diabetes in persons 18 years and older.

The Dexcom G7 15 Day CGM System is intended to replace fingerstick BG testing for diabetes treatment decisions. Interpretation of the Dexcom G7 15 Day CGM System results should be based on the glucose trends and several sequential sensor readings over time. The Dexcom G7 15 Day CGM System also aids in the detection of episodes of hyperglycemia and hypoglycemia, facilitating both acute and long-term therapy adjustments.

The Dexcom G7 15 Day CGM System is also intended to autonomously communicate with digitally connected devices, including automated insulin dosing (AID) systems. The Dexcom G7 15 Day CGM System can be used alone or in conjunction with these digitally connected medical devices for the purpose of managing diabetes.

The Dexcom G7 15 Day Continuous Glucose Monitoring (CGM) System (Dexcom G7 15 Day CGM System or G7 15 Day) is an interoperable continuous glucose monitoring system intended to continuously measure the glucose in the interstitial fluid, calculate the glucose reading and make this available to the user. The Dexcom G7 15 Day CGM System is intended for single patient use at home and requires a prescription.

The G7 15 Day consists of the following primary components: a wearable, consisting of a sensor and transmitter worn on the body and a display device, which can be a G7 Mobile Application (Mobile App) on an iOS or Android OS smart device or a G7 Receiver (Receiver).

To achieve the intended functions and performance of the G7 15 Day, one sensor and at least one display device (App or Receiver) must be used together. The user must pair the display device(s) with each unique sensor to enable communication and start a sensor session. During an active session, the sensor reports new glucose data to the display device every 5-minutes. The display device then displays glucose data and provides alerts and information signals to the user. The reportable glucose range for the G7 15 Day is 40 mg/dL to 400 mg/Dl. Glucose values below this range are reported as 'LOW' and glucose values above this range are reported as 'HIGH'. The sensor has an expected wear period of up to 15 days with an extended 12-hour grace period after the sensor session. The grace period allows additional time for the user to change the sensor at a convenient time.

The Dexcom G7 15 Day CGM System is an interoperable connected device that can communicate glucose readings and other information wirelessly and securely to and from compatible electronic interfaces via the following secure wireless connections:

• Wireless communication from the transmitter directly to an interoperable device communicating through the same protocol
• The Mobile App communicates to another app on a single mobile platform
• The Mobile App communicates through the cloud to another software device
  • Dexcom Partner Web APIs: The Dexcom Partner Web APIs enable secure and reliable communication of CGM data to authorized client software intended to receive the data through the cloud. The Partner Web APIs is not intended to be used by automated insulin delivery systems (AID).

Principle of Operation:

The principles of operation for the Dexcom G7 15 Day CGM System remain the same as prior generations of Dexcom CGM Systems. The System uses a wire-type sensing mechanism that continuously measures interstitial glucose levels and uses a radio transmitter to wirelessly communicate glucose data to the display device for the user to see and use accordingly.

The provided FDA 510(k) clearance letter and summary for the Dexcom G7 15 Day CGM System outlines several acceptance criteria and the studies performed to meet them. However, it's important to note that the document primarily focuses on demonstrating substantial equivalence to a predicate device and fulfilling regulatory requirements. It does not provide the detailed numerical performance metrics typically found in a full clinical study report, nor does it specify exact numerical "acceptance criteria" in all cases, instead referring to meeting "specifications" or "special controls."

Here's an attempt to extract the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

The document states:

• "A clinical study was conducted to evaluate the safety and effectiveness of the Dexcom G7 15 Day CGM System... in adult (18 years and older) participants with diabetes."
• "A subsequent clinical study was performed to assess the impact of a new sensor patch intended to improve survival rate."
• "A separate clinical study was performed in adults with type 1 and type 2 diabetes to assess the data transmission reliability over the 15-day wear period."

Sample Size: The exact sample size (number of participants) for these clinical studies is not specified in the provided text.

Data Provenance:

• Country of Origin: Not specified in the document.
• Retrospective or Prospective: Clinical studies are generally prospective, especially when evaluating safety and effectiveness of a new device. The phrasing "A clinical study was conducted to evaluate..." implies a prospective study.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

This information is not provided in the given document. For a CGM device, the ground truth for glucose measurements is typically established using a highly accurate reference method, such as a YSI glucose analyzer, rather than expert interpretation of images or observations.

4. Adjudication Method for the Test Set

Adjudication methods like "2+1" or "3+1" are typically used in studies involving subjective assessment (e.g., image interpretation by multiple readers). For a continuous glucose monitoring system, the ground truth is established by objective laboratory measurements (YSI). Therefore, an adjudication method for the test set in the traditional sense is not applicable or described.

5. Multi Reader Multi Case (MRMC) Comparative Effectiveness Study

An MRMC study is relevant for devices where human readers interpret data, often with or without AI assistance.

• Was an MRMC study done? No, an MRMC study was not described. The Dexcom G7 is a diagnostic device that provides quantitative glucose values, not an imaging or interpretive AI, so comparative effectiveness with human readers in the traditional MRMC sense is not relevant here.
• Effect size of human readers improvement with AI vs without AI assistance: Not applicable, as no MRMC study was conducted or described for this type of device.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

Yes, a standalone performance assessment was done. The clinical study evaluating "safety and effectiveness of the Dexcom G7 15 Day CGM System... with respect to reference venous plasma sample YSI measurements" directly assesses the algorithm's performance without human interpretation of the CGM readings. The device is intended "to generate continuous glucose measurements and make this available to the user," and the study uses these generated measurements against a gold standard.

7. Type of Ground Truth Used

The primary ground truth for the clinical effectiveness study was:

• Reference venous plasma sample YSI measurements.
  This is a highly accurate laboratory method for measuring glucose in blood, considered the gold standard for comparing CGM performance.

8. Sample Size for the Training Set

The document focuses on the new clinical data submitted for the Dexcom G7 15 Day CGM System and refers to its substantial equivalence to the predicate "Dexcom G7 Continuous Glucose Monitoring System." The sample size for the training set (i.e., data used to develop the algorithms for the CGM) is not mentioned in this 510(k) summary. This information would typically be developed during the device's original design and development phase, not necessarily recounted in each subsequent 510(k) submission unless there were significant algorithm changes requiring new training data.

9. How the Ground Truth for the Training Set Was Established

Similar to item 8, the process for establishing ground truth for the training set used during the development of the Dexcom G7 algorithms is not described in this document. It is highly probable that similar reference methods (like YSI venous plasma measurements) would have been used during the development and training phases as well.

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The Signos Glucose Monitoring System is an over-the-counter (OTC) mobile device application that receives data from an integrated Continuous Glucose Monitor (iCGM) sensor and is intended to continuously measure, record, analyze, and display glucose values in people 18 years and older not on insulin. The Signos Glucose Monitoring System helps to detect normal (euglycemic) and low or high (dysglycemic) glucose levels. The Signos Glucose Monitoring System may also help the user better understand how lifestyle and behavior modification, including diet and exercise, impact glucose excursions. This information may be useful in helping users to maintain a healthy weight.

The user is not intended to take medical action based on the device output without consultation with a qualified healthcare professional.

The Signos Glucose Monitoring System is a mobile device application that is paired, via Bluetooth®, with an over-the-counter interoperable continuous glucose monitor (iCGM). The application functions as a primary display for the iCGM by showing the user's glucose reading along with a historic trend every 15 minutes. The system is capable of backfilling missed data and supporting a grace period dictated by the iCGM.

The system's various displays, text, graphs, suggestions, and notifications serve to clearly illustrate the user's past and present glucose readings and their trend direction to assist the user in maintaining a euglycemic state.

The glucose display range is 70 mg/dL to 250 mg/dL.

The Signos System is intended for users over the age of 18 not on insulin.

The provided text is a 510(k) premarket notification letter from the FDA regarding the Signos Glucose Monitoring System. It primarily focuses on the device's substantial equivalence to a predicate device based on its intended use, technological characteristics, and non-clinical testing.

Unfortunately, the provided document does not contain the detailed information required to describe the acceptance criteria and the study that proves the device meets those criteria, specifically for performance metrics like accuracy or effectiveness related to AI/algorithm performance. The document is a regulatory clearance letter, not a detailed study report.

Here's what can be inferred from the document and what information is missing:

What the document does provide:

• Device Name: Signos Glucose Monitoring System
• Intended Use: Over-the-counter (OTC) mobile device application that receives data from an integrated Continuous Glucose Monitor (iCGM) sensor. Intended to continuously measure, record, analyze, and display glucose values in people 18 years and older not on insulin. Helps detect normal/low/high glucose levels and understand how lifestyle impacts glucose excursions. Not intended for medical action without consultation.
• Technological Characteristics: Software system, displays interstitial fluid glucose sensor data, assists in understanding lifestyle impact on glucose. Uses data from an iCGM (same as predicate). Display range: 70-250 mg/dL. Update interval: Every 15 minutes.
• Non-Clinical Testing Mentioned:
  • Software Testing: Verified that the system functions consistently with design inputs and that displayed data is the same as transmitted data. (This is a functional verification, not a performance study against specific acceptance criteria for diagnostic accuracy)
  • Cybersecurity Testing: Demonstrated no unacceptable cybersecurity risks.
  • Usability / Human Factors: Demonstrated unacceptably low risks related to use errors that could cause harm or degrade performance.

What the document does not provide, and therefore cannot be filled:

1. A table of acceptance criteria and the reported device performance: The document mentions "software requirements have been verified," but does not list specific performance acceptance criteria for glucose measurement accuracy (e.g., MARD, Clarke Error Grid analysis) or how the algorithm detects normal/low/high glucose levels beyond simply displaying the iCGM data. It states the displayed data is the same as transmitted, implying the software's role is primarily display and analysis, not independent glucose measurement.
2. Sample size used for the test set and the data provenance: Not mentioned.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable based on the information provided, as the software is stated to display data transmitted by the biosensor, not to perform independent diagnostic interpretations requiring expert ground truth.
4. Adjudication method: Not applicable.
5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance: Not mentioned and unlikely given the device's described function as a display and analysis tool for iCGM data, rather than an AI diagnostic aid for image interpretation.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: The document describes "Software Testing" which confirms the software displays data correctly, but this is not a standalone diagnostic performance study using an algorithm to interpret data independently of the iCGM. The device's "algorithm" here seems to be in the display and analysis of iCGM data (e.g., trend direction, identifying dysglycemic states based on thresholds), not in generating novel glucose measurements.
7. The type of ground truth used: Not explicitly stated for any actual performance metrics. The software testing confirmed data consistency with the biosensor, implying the biosensor's output is the "truth" for the software. For general "detection of euglycemic/dysglycemic" states, presumably standard glucose thresholds would be used.
8. The sample size for the training set: Not mentioned. The document describes software verification, cybersecurity, and human factors testing, not machine learning model training and validation.
9. How the ground truth for the training set was established: Not mentioned.

Conclusion:

The provided FDA letter grants marketing clearance based on substantial equivalence, primarily asserting that the Signos Glucose Monitoring System is a mobile application that accurately displays data from a legally marketed and cleared iCGM. It emphasizes software functionality, cybersecurity, and usability rather than presenting de novo clinical performance data for an AI/algorithm that performs diagnostic interpretations. The letter likely relies on the predicate iCGM's established performance for glucose measurement, with the Signos system's "performance" being its accurate receipt, display, and basic analysis of that underlying data. Therefore, the detailed performance data and acceptance criteria typical for AI-driven diagnostic devices are not present in this regulatory clearance document.

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TeleRPM Gen2 Blood Glucose Monitoring System is comprised of the TeleRPM Gen2 Blood Glucose Meter and the TeleRPM Blood Glucose Test Strips. TeleRPM Gen2 Blood Glucose Monitoring System is intended to quantitatively measure the glucose concentration in fresh capillary whole blood samples drawn from the fingertips. It is intended for use by persons with diabetes at home as an aid to monitor the effectiveness of diabetes control. It is not intended for neonatal use or for the diagnosis of or screening for diabetes. This system is intended for self-testing outside the body (in vitro diagnostic use), and should only be used by a single person and should not be shared.

TeleRPM Gen2 Blood Glucose Monitoring System consists of TeleRPM Gen2 Blood Glucose Meter and the TeleRPM Blood Glucose Test Strips.

TeleRPM Control Solution, TeleRPM Lancing Device, TeleRPM Lancets are required for use but not included in meter box or test strips box and should be purchased separately. The TeleRPM Control Solution is for use with the above meter and test strip as a quality control check to verify that the meter and test strip are working together properly, and that the test is performing correctly. TeleRPM Lancing Device and TeleRPM Lancets are used for puncturing fingertip and then user can perform qlucose test with blood sample.

TeleRPM Gen2 Blood Glucose Monitoring System is designed to quantitatively measure the glucose concentration in fresh capillary whole blood from the fingertip. The glucose measurement is achieved by using the amperometric detection method. The test is based on measurement of electrical current caused by the reaction of the glucose with the reagents on the electrode of the test strip. The blood sample is pulled into the tip of the test strip through capillary action. Glucose in the sample reacts with glucose oxidase and the mediator. Electrons are generated, producing a current that is positive correlation to the glucose concentration in the sample. After the reaction time, the glucose concentration value is reported in plasma equivalents and is displayed on meter screen.

The provided text primarily focuses on the substantial equivalence determination for the TeleRPM Gen2 Blood Glucose Monitoring System to a predicate device. While it mentions the general types of studies conducted (robustness, precision, linearity, user evaluation, interference, stability, flex studies, software, cybersecurity controls, and a clinical usability study), it does not provide detailed acceptance criteria or numerical performance data as requested for several of your points.

Based on the information available:

1. A table of acceptance criteria and the reported device performance:

The document broadly states that the device "met the FDA SMBG OTC Guidance and industry standards" and that "these devices performed as intended and met associated guidance documents and industry standards." Specific numerical acceptance criteria and reported device performance for each study (precision, linearity, interference, etc.) are not detailed in the provided summary. For the user evaluation (clinical study), it states "the clinical performance met the FDA SMBG OTC Guidance."

2. Sample size used for the test set and the data provenance:

• User Evaluation (Clinical Study): The document mentions "All participants" were able to understand the labeling, use the system, and interpret results. However, the exact sample size for the clinical usability evaluation is not specified.
• Provenance: Not explicitly stated, but the company is located in Zhongshan, Guangdong, China. The testing location isn't specified, but it's reasonable to infer the studies were conducted by or on behalf of the manufacturer, likely in China or a region where they operate.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• This information is not provided in the summary. For a blood glucose monitoring system, the "ground truth" for glucose levels would typically be established by a laboratory reference method, not by experts adjudicating results.
• For the usability evaluation, the "ground truth" is about successful interaction with the device, which is assessed through user performance and observation, not expert consensus on a measurement.

4. Adjudication method for the test set:

• This information is not provided. For analytical performance, laboratory reference methods are used, not typically expert adjudication. For usability, the success of user interaction is observed and recorded.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• This is not applicable to this device. A "Blood Glucose Monitoring System" measures blood glucose; it is not an AI-assisted diagnostic imaging device that involves "human readers." Therefore, an MRMC study comparing human readers with and without AI assistance was not performed.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

• The device performs a direct measurement of blood glucose. Its core function is a "standalone" algorithmic interpretation of the electrochemical reaction to display a glucose reading. This is its fundamental operation. There isn't a separate "human-in-the-loop" component in the direct glucose measurement process that would necessitate a distinction here.

7. The type of ground truth used:

• For analytical performance (precision, linearity, interference), the ground truth for blood glucose concentration would be established using a laboratory reference method (e.g., YSI analyzer). This is standard for blood glucose meter validation.
• For the usability evaluation, the "ground truth" assesses whether users can successfully operate the device and interpret results, which is based on direct observation and participant feedback.

8. The sample size for the training set:

• This device is a physical blood glucose meter and test strips relying on electrochemical principles, not a machine learning or AI model that requires a "training set" in the computational sense. Therefore, the concept of a training set sample size is not applicable here.

9. How the ground truth for the training set was established:

• As above, the concept of a training set is not applicable.

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The XPER Technology PREMIUM Pro Blood Glucose Monitoring System is intended for point-of-care, in vitro diagnostic, multiple-patient use for the quantitative determination of glucose in fresh capillary whole blood samples from the fingertips in endocrinology clinic laboratories and physician office laboratories.

The system should only be used with single-use, auto-disabling lancing devices when performing a fresh capillary whole blood sample from the fingertip.

The system is not intended for the screening or diagnosis of diabetes mellitus but is indicated for use in determining dysglycemia.

The system is not intended for use on patients receiving intensive medical intervention/therapy.

The system is not intended for use in acute care, nursing facilities, skilled nursing facilities or hospital settings. The system is not intended for use on neonates.

The XPER Technology PREMIUM Pro Blood Glucose Monitoring System consists of the XPER Technology PREMIUM Pro Blood Glucose Meter, the XPER Technology PREMIUM Pro Blood Glucose Test Strips, and the TaiDoc Blood Glucose Control Solutions. This system is a multiplepatient use for the quantitative determination of glucose in capillary whole blood samples from the fingertips in endocrinology clinic laboratories and physician office laboratories as an aid in monitoring the effectiveness of glucose control. The TaiDoc Blood Glucose Control Solutions are used to check that the meter and test strips are working together properly.

Here's a breakdown of the acceptance criteria and study details for the XPER Technology PREMIUM Pro Blood Glucose Monitoring System, based on the provided FDA 510(k) summary:

Acceptance Criteria and Device Performance

The acceptance criteria are implicitly defined by the statement "all test results were within acceptance criteria" in various sections. The reported performance is the achievement of these criteria. The specific numerical targets for each criterion are not always explicitly stated (e.g., for precision, linearity, hematocrit, interference), but the document confirms that the device met these criteria.

Table of Acceptance Criteria and Reported Device Performance

Study Details

This document describes a medical device, a Blood Glucose Monitoring System, which does not utilize AI or involve human readers for image interpretation. Therefore, questions related to AI models, human reader improvement with AI assistance, expert adjudication for ground truth related to image analysis, or MRMC studies are not applicable to this device.

Here's the relevant information based on the provided text:

1. Sample sizes used for the test set and data provenance:

   • Clinical Accuracy (Capillary Blood): 414 patients.
     • Provenance: Clinical study conducted at 3 U.S. sites and 6 Taiwan sites. Data is prospective as it was collected during an active clinical study with patients.
   • Accuracy at Extremes: 100 samples (50 for 300 mg/dL).
     • Provenance: "Blood samples were collected and allowed to glycolyze or were spiked with high concentration glucose solution". Implies a laboratory-controlled, prospective or specially prepared sample set.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not Applicable in the traditional sense of 'experts' interpreting data. For blood glucose monitoring systems, the "ground truth" is established by a highly accurate reference method.
   • The ground truth for the clinical and extreme glucose value studies was the YSI Model 2300 Glucose Analyzer. This is a laboratory-based, well-established, and highly accurate reference method for glucose measurement, not human experts.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

   • Not Applicable. As ground truth is established by an automated reference analyzer (YSI-2300), there is no human interpretation or adjudication process involved in setting the ground truth for glucose values.

4. If a multi reader multi case (MRMC) comparative effectiveness study was done, if so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • Not Applicable. This is a blood glucose monitoring system, not an AI-assisted diagnostic imaging device. There are no human readers or AI assistance in the interpretation of results from this device in the same way as an imaging study.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

   • Yes, in essence. Blood glucose meters are inherently "standalone" in their function of measuring glucose. The clinical study directly compares the device's numerical output (algorithm's result) to the YSI-2300 reference method without human interpretation of the device's output influencing the direct comparison. The "human-in-the-loop" for this device is the user taking and reading the measurement, but the performance evaluation is on the accuracy of the numerical reading itself, independent of user interpretation for the primary outcome.

6. The type of ground truth used:

   • Reference Method: The YSI Model 2300 Glucose Analyzer, a laboratory-based, highly accurate method for quantitative glucose determination. Comparisons are quantitative.

7. The sample size for the training set:

   • Not directly applicable/not explicitly stated in terms of an "AI training set." This device is a traditional electrochemical biosensor, not an AI/machine learning model that undergoes a distinct "training" phase with a large dataset in the way a deep learning algorithm would. The development and calibration of such a device involve extensive laboratory testing and optimization, which could be considered an analogous "training" or development process for its internal algorithms, but it's not described as a separate, quantifiable "training set" with ground truth in the context of AI regulatory submissions.

8. How the ground truth for the training set was established:

   • Not applicable in the AI context. For this type of device, ground truth for development/calibration (analogous to "training") would be established through a combination of:
     • Highly controlled laboratory experiments using reference solutions of known glucose concentrations.
     • Comparisons to established reference methods (like YSI-2300) with well-characterized samples.
     • Controlled studies to characterize and mitigate interferences (e.g., hematocrit, medications).

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RIGHTEST Blood Glucose Monitoring System Max Tel is intended to the quantitative measurement of glucose (sugar) in fresh capillary whole drawn from the fingertips, forearm, or palm. It is intended to be used by a single person and should not be shared.

RIGHTEST Blood Glucose Monitoring System Max Tel is intended for self- testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid to montor the effectiveness of diabetes control. It should not be used for the diagnosis of, or screening for diabetes or for neonatal use. Alternative site testing should be done only during steady-state times (when glucose is not changing rapidly).

The RIGHTEST Blood Glucose Monitoring System Max Tel is comprised of the RIGHTEST Meter Max Tel and the RIGHTEST Blood Glucose Test Strip Max.

RIGHTEST Blood glucose monitoring System Max Tel consists of the following devices: Blood Glucose Meter, Blood Glucose Test Strip, Control Solution, Lancing Device and Sterile Lancets. The Blood Glucose Meter, Blood Glucose Test Strips, and Lancing Device are manufactured by BIONIME Corporation.

RIGHTEST Blood Glucose Meter Max Tel, when used with the RIGHTEST Blood Glucose Test Strips Max, quantitatively measure glucose in fresh whole blood samples from capillary. The performance of RIGHTEST Blood Glucose Monitoring System Max Tel is verified by the RIGHTEST Control Solution GC700.

The glucose measurement is achieved by using the amperometric detection meth test is based on measurement of electrical current caused by the reaction of the glucose with the reagents on the electrode of the test strip. The blood sample is pulled into the tip of the test strip through capillary action. Glucose in the sample reacts with FAD-glucose dehydrogenase and the mediator. Electrons are generated, producing a current that is positive correlation to the glucose concentration in the sample. After the reaction time, the glucose concentration in the sample is displayed.

The provided FDA 510(k) summary for the RIGHTEST Blood Glucose Monitoring System Max Tel focuses on demonstrating substantial equivalence to a predicate device, as opposed to providing detailed clinical study results typical of a de novo or PMA submission. Therefore, much of the requested information regarding a comprehensive study proving acceptance criteria for an AI/device for diagnostic purposes (e.g., number of experts, MRMC studies, ground truth establishment for a training set) is not directly present in this document because it is not an AI/Software as a Medical Device (SaMD) submission for a diagnostic algorithm.

This document describes a glucose monitoring system, which is a medical device rather than an AI-powered diagnostic system that typically involves image analysis or complex algorithmic interpretations of patient data for diagnosis. The "Software Safety Analysis" refers to enabling LTE functionality and adjusting the measurement range, alongside cybersecurity considerations, not the performance of a diagnostic AI.

However, I can extract the acceptance criteria and performance as described in the document for this specific device:

Device: RIGHTEST Blood Glucose Monitoring System Max Tel
Intended Use: Quantitative measurement of glucose (sugar) in fresh capillary whole blood samples for self-testing by people with diabetes at home, as an aid to monitor the effectiveness of diabetes control.

1. Table of Acceptance Criteria and Reported Device Performance

Based on the "Discussion of Non-Clinical Tests Performed for Determination of Substantiability" (Section 8) and the "Comparison to Predicate Devices" (Section 7), the acceptance criteria are generally implied by the successful completion and compliance with relevant FDA guidelines for glucose monitoring systems. The performance is reported in terms of demonstrating compliance.

Overall Conclusion: "Results of performance evaluation of RIGHTEST Blood Glucose Monitoring System Max Tel that had no impacts to BGM measurement was conducted to support substantially equivalent to the predicate device..." |
| Measurement Range: Correct display of "Hi" or "Lo" for out-of-range results. | Hi Lo Display: "The measurement range has been adjusted, and the system displayed a notification indicating 'Hi' or 'Lo'—for results that fall outside the established range." The specific numerical range is 20 - 600 mg/dL (1.1 - 33.3 mmol/L). |
| Software Functionality and Safety:

• Successful implementation and validation of LTE functionality.
• Compliance with FCC testing.
• Compliance with FDA's cybersecurity guidance. | Software Safety Analysis: "Software adjustments were made to enable LTE functionality and adjusted the measurement range. The LTE function was validated through both FCC compliance testing and laboratory testing. As LTE functionality introduced cybersecurity considerations, we ensured compliance with the FDA's guidance on the Content of Premarket Submissions for Management of Cybersecurity in Medical Devices." |
  | Interference: Performance maintained in the presence of specified interferents. | Interference Data Points: Ascorbic Acid ≥ 3 mg/dL, Conjugated Bilirubin ≥ 30 mg/dL, Uric Acid ≥ 12 mg/dL, Xylose ≥ 8 mg/dL. (Implies performance within specification despite these levels, though the exact outcome of the testing is not described beyond listing the tested interferents) |
  | Other Functional Parameters: Measurement technology, sample type, minimum sample volume, test time, control solution compatibility, operating conditions, storage conditions, shelf life, reagent composition, power saving, coding, monitor, backlight, color, power supply, memory capacity, meter dimension, LCD display area, meter weight, data transmission. | All these parameters are listed as characteristics of the new device, implicitly meeting the predicate device's standards or being deemed acceptable (e.g., LTE network for data transmission is a new feature). |
  | General Acceptance: All laboratory studies met acceptance criteria. | "All laboratory studies that the acceptance criteria were met. Therefore, the performances from these laboratory studies were acceptable." |

Regarding the other requested points (relevant for AI/SaMD):

• 2. Sample sized used for the test set and the data provenance: Not specified in the provided document. The reference to "natural and modified blood samples" in "The Extreme Glucose Study" suggests lab-based testing, but no specific sample size or provenance is given.
• 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. This is not an AI/diagnostic imaging device requiring expert ground truth for interpretation. Ground truth for a glucose meter is typically established by laboratory reference methods (e.g., YSI analyzer).
• 4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. This type of adjudication is usually for subjective interpretations by multiple human readers, not for a highly objective measurement device like a glucose meter.
• 5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance: Not applicable. This is not an AI system assisting human readers.
• 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: The device itself is a "standalone" measurement device. Its performance is measured directly against laboratory reference standards, but there is no "algorithm only" in the sense of an AI interpreting complex data that a human would usually interpret.
• 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): For glucose meters, the ground truth is typically a laboratory reference method (e.g., a YSI analyzer), rather than expert consensus or pathology, as the measurement is quantitative. This is implied by the nature of the device, although not explicitly stated as "YSI" in the document.
• 8. The sample size for the training set: Not applicable. This device does not use machine learning with a distinct training set in the typical sense of an AI/ML algorithm. Its functionality is based on established electrochemical principles, not pattern recognition learned from a dataset.
• 9. How the ground truth for the training set was established: Not applicable, for the same reason as point 8.

In summary, the provided document is a 510(k) summary for a blood glucose monitoring system, emphasizing its substantial equivalence to a predicate device and compliance with general FDA guidelines for such devices. It does not contain the detailed study results and AI-specific ground truth methodologies that would be found in a submission for an AI-powered diagnostic device.

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The Sejoy Blood Glucose Monitoring System is composed of the Sejoy Blood Glucose Meter and Sejoy Blood Glucose Test Strips. The Sejoy Blood Glucose Monitoring System is intended to be used for the quantitative measurement of glucose in fresh capillary whole blood collected from the fingertip. The Sejoy Blood Glucose Monitoring System is intended for self-testing outside the body (in-vitro diagnostic use), by individuals with diabetes at home as an aid to monitor the effectiveness of diabetes control. This system is intended to be used by a single person and should not be shared. The system should not be used for the diagnosis of, or screening for diabetes or for neonatal use.

The Sejoy Advance Link Blood Glucose Monitoring System is composed of the Sejoy Advance Link Blood Glucose Meter and Sejoy Blood Glucose Test Strips. The Sejoy Advance Link Blood Glucose Monitoring System is intended to be used for the quantitative measurement of glucose in fresh capillary whole blood drawn from the fingertips. The Sejoy Advance Link Blood Glucose Monitoring System is intended for self-testing outside the body (in-vitro diagnostic use), by individuals with diabetes at home as an aid to monitor the effectiveness of diabetes control. This system is intended to be used by a single person and should not be shared. The system should not be used for the diagnosis of, or screening for diabetes or for neonatal use.

The Sejoy Advance Link Blood Glucose Monitoring System is composed of the Sejoy Advance Link Blood Glucose Meter and Sejoy Blood Glucose Test Strips, and the Sejoy Blood Glucose Monitoring System is composed of the Sejoy Blood Glucose Meter and Sejoy Blood Glucose Test Strips. The Sejoy Blood Glucose Control Solutions, and the Sejoy Lancing Device with Sejoy disposable safety lancets (K222034, manufactured independently by Beijing Ruicheng Medical Supplies Co. Ltd. and cleared under 510(k)) are for use with the system and could sold separately.

The Sejoy Advance Link Blood Glucose Meter and Sejoy Blood Glucose Meter differ only in Bluetooth functionality which is present only in the Sejoy Advance Link Blood Glucose Meter.

The system measures glucose using amperometric technology and features glucose dehydrogenase in the test strip, interacting with glucose in the blood to produce an electrical current. This current is directly proportional to the blood glucose concentration, converted into values by the system software. The result is displayed on the meter's LCD in plasma value equivalence (mg/dL) and is automatically stored.

The provided document describes the Sejoy Blood Glucose Monitoring System and Sejoy Advance Link Blood Glucose Monitoring System and their substantial equivalence to a predicate device. Information relevant to acceptance criteria and study proving performance is extracted below.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for blood glucose monitoring systems are primarily based on accuracy compared to a lab reference method. The document specifies accuracy levels at various percentage tolerances.

| Accuracy Tolerance | Acceptance Criterion (Implicit) | Reported Device Performance (Overall) | Reported Device Performance (Glucose 250 mg/dL) |
|---|---|---|---|---|
| Within ±5% | N/A (Often implies higher percentages) | 56.8% (200/352) | 54.0% (27/50) | 60.0% (30/50) |
| Within ±10% | N/A (Often implies higher percentages) | 90.3% (318/352) | 92.0% (46/50) | 96.0% (48/50) |
| Within ±15% | N/A (Often implies higher percentages) | 98.3% (346/352) | 100.0% (50/50) | 100.0% (50/50) |
| Within ±20% | N/A (Often implies higher percentages) | 100% (352/352) | 100.0% (50/50) | 100.0% (50/50) |

Note: The document states "Sejoy Blood Glucose Monitoring System and Sejoy Advance Link Blood Glucose Monitoring System were designed and tested in accordance with FDA Guidance: Self-Monitoring Blood Glucose Test Systems for Over-the-Counter Use (September 2020)." This guidance typically sets specific accuracy criteria (e.g., within ±15% for a certain percentage of samples). While the exact numerical criteria from the FDA guidance are not explicitly stated in the provided text, the reported performance metrics clearly indicate the device's adherence to such guidelines, as implied by the phrase "sufficiently accurate."

2. Sample Size Used for the Test Set and Data Provenance

• Overall Test Set Sample Size: 352 lay persons (for the user evaluation study).
• Extreme Glucose Concentrations Test Set Sample Size: 50 subjects for low blood glucose (250 mg/dL), totaling 100 subjects for this specific sub-study.
• Data Provenance: The document does not explicitly state the country of origin for the data, but it refers to "lay persons representative of the age, gender, education of the intended users in the US," suggesting the study subjects were recruited in the US. The study was a prospective user performance evaluation where subjects self-tested their capillary whole blood.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

The ground truth was established using a laboratory reference method, the YSI 2300 STAT PLUS glucose analyzer, which is a highly accurate and standardized instrument. There is no mention of experts being used to establish the ground truth for the test set, as the YSI analyzer itself serves as the gold standard.

4. Adjudication Method for the Test Set

Not applicable. The ground truth was established by a laboratory reference instrument (YSI 2300), not by human readers requiring adjudication.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done, What was the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

Not applicable. This device is a blood glucose monitoring system, not an AI-assisted diagnostic imaging or interpretation tool. The study involved users operating the device, not interpreting images with or without AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Yes, analytical performance testing was conducted which can be considered analogous to "standalone" performance for such a device. This included:

• Repeatability
• Intermediate precision
• Hematocrit effect
• Short sample volume
• Perturbation
• Interference
• Linearity testing

These tests evaluate the intrinsic performance of the device's measurement algorithm and hardware components, independent of human operation variability.

7. The Type of Ground Truth Used

The ground truth was established using a laboratory reference method: the Yellow Springs Instrument (YSI 2300 STAT PLUS glucose analyzer). The document specifies "capillary plasma" for the YSI 2300 reference, indicating a highly accurate and controlled measurement.

8. The Sample Size for the Training Set

The document describes performance evaluation studies (user evaluation and extreme glucose concentration studies) which are typically "test set" studies for device clearance. It does not provide information on the sample size used for the training set of the device's internal algorithms, as this detail is generally considered proprietary to the manufacturer and not typically included in a 510(k) summary unless the device heavily relies on a continuously learning AI model that requires explicit training data disclosure in the submission. For a blood glucose meter, the "training" (calibration and optimization) of its algorithms is usually done during the device's development phase rather than through a distinct "training set" in the context of machine learning.

9. How the Ground Truth for the Training Set Was Established

Since information about a specific "training set" is not provided, the method for establishing its ground truth is also not detailed. However, for a device like a blood glucose meter, the internal algorithms and calibration are established using highly controlled laboratory experiments and reference methods (like YSI 2300) during the design and development phase to ensure accuracy across the measurement range and various physiological conditions.

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The Eversense 365 Continuous Glucose Monitoring (CGM) System is indicated for continually measuring glucose levels for up to 1 year in people (18 years or older) with diabetes. The system is indicated for use to replace fingerstick blood glucose measurements for diabetes treatment decisions.

The system is intended to:

· Provide real-time glucose readings.

· Provide glucose trend information.

· Provide alerts for the detection of episodes of low blood glucose (hypoglycemia) and high blood glucose (hyperglycemia).

Historical data from the system can be interpreted to aid in providing therapy adjustments. These adjustments should be based on patterns and trends seen over time.

The Eversense 365 CGM System is also intended to autonomously communicate with digitally connected devices, including automated insulin dosing (AID) systems. The Eversense 365 CGM System can be used alone or in conjunction with these digitally connected medical devices for the purpose of managing diabetes.

The system is intended for single patient use and requires a prescription.

The Eversense 365 Continuous Glucose Monitoring (CGM) System provides continuous glucose measurements over a 40-400 mg/dL range. The system calculates glucose, trends and provides alerts for high and low glucose available for display on a mobile platform. It consists of a glucose Sensor (the Eversense 365 Sensor) that is inserted under the skin using Insertion Tools; an externally worn Eversense 365 Smart Transmitter); and the Eversense 365 App, which runs on a compatible handheld device (HHD), such as a smartphone. The inserted Sensor is a radiofrequency (RF)-powered device that collects readings and sends them to the Transmitter. The Transmitter calculates, stores, and transmits the glucose data via Bluetooth Low Energy (BLE) to the Eversense 365 App on an HHD.

The CGM System consists of three principal components.

• 1. Sensor: The Sensor, inserted subcutaneously, receives RF-power from the Transmitter to measure interstitial fluid glucose every 5 minutes. The Sensor sends fluorescence readings or data to the Transmitter for calculation and storage of glucose values. The Sensor has a silicone collar component that contains an anti-inflammatory steroid drug (dexamethasone acetate) that elutes locally to reduce tissue inflammation around the Sensor. The Sensor operating life is up to one year or until the device's end-of-life is reached, whichever occurs first. The Sensor is provided sterile, for single use in a Sensor Holder. The Sensor is inserted using the provided Insertion Tools.
• 2. Transmitter: The Transmitter, worn externally over the inserted Sensor, is a device with rechargeable battery that powers the Sensor, calculates the glucose values from the Sensor-measured fluorescence readings, and using secure BLE wirelessly sends the glucose information to the Eversense 365 App for display on the HHD. An adhesive patch holds the Transmitter in place. The Transmitter is charged using the provided power cord and charge adapter. The Transmitter also provides vibration signals for alerts and notifications, such as low glucose levels, irrespective of whether the Eversense 365 App is in the vicinity or not.
• 3. App: The Eversense 365 App is a software application that runs on an HHD (e.g., compatible mobile device) for display of glucose information provided by the Transmitter. The Eversense 365 App receives and displays the calculated glucose information from the Transmitter, including glucose trend information and glucose alerts. The Eversense 365 App also allows the user to calibrate the CGM System. It also communicates with the Senseonics server for a one-time download of calibration parameters specific for each Sensor following the insertion as part of the linking process. The Eversense 365 App also provides the user an option to upload the data to Senseonics Data Management System (DMS) for historic viewing and storing of glucose data.

The provided text is a summary of a 510(k) premarket notification for the Eversense 365 Continuous Glucose Monitoring (CGM) System. It details the device, its intended use, comparison to a predicate device, and performance data from a clinical study.

Here's a breakdown of the acceptance criteria and study details based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are derived from the "iCGM Special Controls" listed in Tables 4, 5, and 6. The reported device performance is the "Eversense 365 CGM System Performance" and "Eversense 365 Results" columns.

All reported Eversense 365 CGM System Performance values meet or exceed the specified Special Control (acceptance criteria) thresholds.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Test Set: 110 adult subjects (ages 18 years and older). The document also refers to Matched Pairs (N) for accuracy metrics, which sum up to 40497 for the overall accuracy, representing individual CGM readings matched with reference glucose values.
• Data Provenance: The ENHANCE Study was a prospective, multi-center study conducted at 4 sites in the United States.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of those Experts

The text does not specify the number of experts or their qualifications used to establish the ground truth. It states that "The accuracy of the Eversense 365 CGM System was evaluated during clinic visits comparing CGM glucose values and plasma glucose values measured using a bedside glucose analyzer."

4. Adjudication Method for the Test Set

The text does not specify an adjudication method. The ground truth was established by a laboratory-based glucose analyzer.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This device is a Continuous Glucose Monitoring (CGM) system, which directly measures glucose and provides readings, trend information, and alerts. It does not involve human readers interpreting images or data with or without AI assistance in the context of diagnostic imaging. The "AI" component would be the algorithm within the CGM system itself (e.g., for calculating glucose values from sensor readings, trend analysis, and alerts), not an interpretative aid for human readers.

6. If a Standalone (i.e. algorithm only without human-in-the loop performance) was done

Yes, the performance data presented (Tables 4-6) reflect the standalone performance of the Eversense 365 CGM System. The accuracy metrics compare the device's generated glucose values directly against a laboratory reference method (YSI 2300 Stat Plus Glucose & Lactate Analyzer). The device is designed to "replace fingerstick blood glucose measurements for diabetes treatment decisions" and "autonomously communicate with digitally connected devices" – implying its ability to function without human interpretation of its raw sensor data.

7. The Type of Ground Truth Used

The ground truth used was plasma glucose values measured using a bedside glucose analyzer (specifically, a Yellow Springs Instruments (YSI) 2300 Stat Plus Glucose & Lactate Analyzer, which is an FDA-accepted laboratory-based glucose measurement method). This is a type of reference standard or outcome data (measured physiological parameter).

8. The Sample Size for the Training Set

The text does not provide information regarding the sample size of the training set for the device's algorithms. The clinical study (ENHANCE Study) is described as an evaluation of the device's accuracy and safety, implying it served as a test or validation set rather than a training set.

9. How the Ground Truth for the Training Set Was Established

Since the text does not mention the training set size, it also does not specify how ground truth for any training set was established.

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The CONTOUR® NEXT GEN Blood Glucose Monitoring System consists of the CONTOUR® NEXT GEN meter, CONTOUR® NEXT blood glucose test strips and the CONTOUR® Diabetes app.

The CONTOUR® NEXT GEN Blood Glucose Monitoring System is intended to be used for the quantitative measurement of glucose in fresh capillary whole blood drawn from the fingertips. The CONTOUR® NEXT GEN Blood Glucose Monitoring System is intended to be used by a single person and should not be shared. The CONTOUR® NEXT GEN Blood Glucose Monitoring System is intended for self-testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid in monitoring the effectiveness of a diabetes control program.

The CONTOUR® NEXT GEN Blood Glucose Monitoring System should not be used for the diagnosis of or screening for diabetes or for neonatal use.

The system is intended for in vitro diagnostic use only.

The CONTOUR® PLUS BLUE Blood Glucose Monitoring System consists of the CONTOUR® PLUS BLUE meter, the CONTOUR® PLUS blood qlucose test strips, and the CONTOUR® Diabetes app.

The CONTOUR® PLUS BLUE Blood Glucose Monitoring System is intended to be used for the quantitative measurement of glucose in fresh capillary whole blood drawn from the fingertips. The CONTOUR® PLUS BLUE Blood Glucose Monitoring System is intended to be used by a single person and should not be shared. The CONTOUR® PLUS BLUE Blood Glucose Monitoring System is intended for self-testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid in monitoring the effectiveness of a diabetes control program.

The CONTOUR® PLUS BLUE Blood Glucose Monitoring System should not be used for the diagnosis of or screening for diabetes or for neonatal use. The CONTOUR® PLUS blood glucose test strips are for use with the CONTOUR® PLUS BLUE meter to quantitatively measure glucose in fresh capillary whole blood drawn from the fingertips.

The system is intended for in vitro diagnostic use only.

CONTOUR® NEXT GEN and CONTOUR® PLUS BLUE Blood Glucose Meters have Bluetooth Low Energy technology built in so that the meters can communicate wirelessly to smart phones and tablets. The CONTOUR® NEXT GEN meter uses the CONTOUR® NEXT blood glucose test strips and CONTOUR® NEXT control solution and CONTOUR® PLUS BLUE meter uses CONTOUR® PLUS blood glucose test strips and CONTOUR® PLUS control solution respectively. The meters can be connected to the CONTOUR® Diabetes app. Both the meters use two replaceable coin cell batteries. Both the meters' shape is a traditional oval form factor. The CONTOUR® NEXT GEN and CONTOUR® PLUS BLUE meters have smartLIGHT® and smartCOLOR® indicator features respectively to see if a glucose result is above, within, or below target range.

Here's an analysis of the provided text regarding the acceptance criteria and study for the CONTOUR® PLUS BLUE and CONTOUR® NEXT GEN Blood Glucose Monitoring Systems.

It's important to note that the provided FDA 510(k) clearance letter and summary primarily focus on demonstrating substantial equivalence to a predicate device, specifically for a minor modification (change in Bluetooth Low Energy microprocessor). As such, the documentation does not contain exhaustive details about the initial validation studies that established the device's fundamental accuracy and performance. Instead, it leverages previous clearances and focuses on showing that the change doesn't negatively impact performance.

Therefore, for several points requested in the prompt, the information is not available in the provided text, as the submission is for a modification rather than an entirely new device's initial clearance.

Acceptance Criteria and Device Performance

The document states that "Bench testing showed that the CONTOUR® NEXT GEN Blood Glucose Monitoring System and CONTOUR® PLUS BLUE Blood Glucose Monitoring System performed as intended and met the relevant standards (ANSI IEEE C63.27-2021, IEEE UL Std 2621.2-2022, IEC 60601-1-2 Edition 4.1 2020-09 CONSOLIDATED VERSION), performance testing and software testing applicable to this change."

While specific numerical acceptance criteria (e.g., accuracy percentages) and detailed reported performance metrics are not explicitly listed in this 510(k) summary, the mention of "relevant standards" and "performance testing" implies that the device met the established performance requirements for blood glucose monitoring systems. For the purpose of this specific modification submission, the critical acceptance criterion was demonstrating that the measurement function was not impacted by the change.

Given the nature of the submission (a change in microprocessor), the primary 'acceptance criteria' in this context are:

Note: For a full, initial clearance of a blood glucose meter, specific accuracy criteria (e.g., ISO 15197) would be provided, often specifying percentages of readings within a certain deviation from a reference method (e.g., laboratory analyzer) for different glucose ranges. These details are not in the provided modification summary.

Study Details

1. Sample size used for the test set and the data provenance:

   • Sample Size: Not specified for this particular submission's testing. The document states "Bench testing including reliability testing, software verification and validation, and confirmation of no impacts to BGM measurement was conducted." This type of testing typically involves a set number of meters and strips, and controlled blood samples, but the exact quantities are not detailed in this summary.
   • Data Provenance: Not explicitly stated (e.g., country of origin). The testing described is "bench testing," implying laboratory-based evaluation. The document also states "The modified devices also relied on previously conducted analytical testing to support substantial equivalence." This suggests some data would be retrospective from prior clearances. The "clinical testing was leveraged from the previous clearances," meaning no new clinical trials were conducted for this specific modification.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not Applicable / Not Specified. For a blood glucose meter, "ground truth" for accuracy is typically established by comparative measurements against a laboratory reference method (e.g., a YSI Glucose Analyzer) using blood samples with known glucose concentrations, not by expert consensus on visual review as might be the case for imaging devices. The document does not describe the specific ground truth establishment method for the bench testing beyond stating "confirmation of no impacts to BGM measurement." For the leveraged clinical testing (from previous submissions), the ground truth would have been established using a laboratory reference method, but the details are not provided here.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

   • Not Applicable. Adjudication methods like 2+1 or 3+1 are used in studies involving human interpretation of data (e.g., radiology reads) to resolve discrepancies. This document describes bench testing for a physical/electrical device modification and leveraging prior clinical data, neither of which involves such adjudication processes.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • No. This is a blood glucose monitoring system, not an AI-powered image analysis or diagnostic tool involving human readers. Therefore, an MRMC study is not relevant or performed for this device.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

   • Partially Applicable. Blood glucose meters are essentially standalone algorithms (or systems) that provide a numerical output. The "bench testing" and "confirmation of no impacts to BGM measurement" assessed the device's performance directly, independent of a human "in the loop" for the measurement itself, beyond the act of sampling. The focus was on the performance of the meter and strips, and the impact of the new microprocessor on that performance.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • Analytical Reference Method. For blood glucose meters, the ground truth for accuracy is established by a highly accurate laboratory reference method (e.g., YSI Glucose Analyzer) that measures glucose concentration in blood samples. This is a scientific, analytical measurement, not based on expert consensus or pathology. While not explicitly detailed for this submission's testing, it would have been the ground truth for the "previously conducted analytical testing" and "clinical testing leveraged from the previous clearances."

7. The sample size for the training set:

   • Not Applicable / Not Specified. This document describes a modification to an existing, cleared device, not the development of a new device or an AI/machine learning model that would have a traditional "training set." The performance assessments are validation efforts, not model training.

8. How the ground truth for the training set was established:

   • Not Applicable. See point 7.

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VivaChek™ Fad Blood Glucose Monitoring System is intended to quantitatively measure the glucose concentration in fresh capillary whole blood samples drawn from the fingertips. It is intended for use by persons with diabetes at home as an aid to monitor the effectiveness of diabetes control. It is not intended for neonatal use or for the diagnosis of or screening for diabetes. This system is intended for self-testing outside the body (in vitro diagnostic use), and should only be used by a single person and should not be shared.

VivaChek™ Fad Smart Blood Glucose Monitoring System is intended to quantitatively measure the glucose concentration in fresh capillary whole blood samples drawn from the fingertips. It is intended for use by persons with diabetes at home as an aid to monitor the effectiveness of diabetes control. It is not intended for neonatal use or for the diagnosis of or screening for diabetes. This system is intended for self-testing outside the body (in vitro diagnostic use), and should only be used by a single person and should not be shared.

VivaChek™ Fad Sync Blood Glucose Monitoring System is intended to quantitatively measure the glucose concentration in fresh capillary whole blood samples drawn from the fingertips. It is intended for use by persons with diabetes at home as an aid to monitor the effectiveness of diabetes control. It is not intended for neonatal use or for the diagnosis of or screening for diabetes. This system is intended for self-testing outside the body (in vitro diagnostic use), and should only be used by a single person and should not be shared.

VivaChek™ Fad Blood Glucose Monitoring System consists of VivaChek™ Fad Blood Glucose Meter and the VivaChek™ Fad Blood Glucose Test Strips. The glucose meter and test strips are packaged separately.

VivaChek™ Fad Smart Blood Glucose Monitoring System consists of VivaChek™ Fad Smart Blood Glucose Meter and the VivaChek™ Fad Blood Glucose Test Strips. The glucose meter and test strips are packaged separately.

VivaChek™ Fad Sync Blood Glucose Monitoring System consists of VivaChek™ Fad Sync Blood Glucose Meter and the VivaChek™ Fad Blood Glucose Test Strips. The glucose meter and test strips are packaged separately.

VivaChek Fad Control Solution, VivaChek Lancing Device, VivaChek Lancets are required for use but not included in meter box or test strips box and should be purchased separately. The VivaChek Fad Control Solution is for use with the above meter and test strip as a quality control check to verify that the meter and test strip are working together properly, and that the test is performing correctly. VivaChek Lancing Device and VivaChek Lancets are used for puncturing fingertip and then user can perform qlucose test with blood sample.

VivaChek™ Fad Blood Glucose Monitoring System, VivaChek™ Fad Smart Blood Glucose Monitoring System and VivaChek™ Fad Sync Blood Glucose Monitoring System are designed to quantitatively measure the glucose concentration in fresh capillary whole blood from the fingertip. The ducose measurement is achieved by using the amperometric detection method. The test is based on measurement of electrical current caused by the reaction of the glucose with the reagents on the electrode of the test strip. The blood sample is pulled into the tip of the test strip through capillary action. Glucose in the sample reacts with glucose dehydrogenase and the mediator. Electrons are generated, producing a current that is positive correlation to the glucose concentration in the sample. After the reaction time, the glucose concentration in the sample is displayed.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document mentions meeting "FDA SMBG OTC Guidance and industry standards" for clinical performance. A specific detailed table of acceptance criteria and reported performance is not explicitly provided in the excerpt. However, based on the context of Blood Glucose Monitoring Systems, the primary acceptance criteria would relate to the accuracy of glucose readings compared to a reference method. The document states that the clinical studies data showed that the clinical performance met the FDA SMBG OTC Guidance, implying the device successfully passed these criteria. Without the specific guidance document referenced, a detailed table cannot be created from this text alone.

2. Sample Size Used for the Test Set and Data Provenance

The document does not explicitly state the numerical sample size used for the test set in the clinical studies. It mentions "non-professional, inexperienced lay persons" were used for user evaluations.

• Data Provenance: The studies were conducted by Vivachek Biotech (Hangzhou) Co., Ltd, located in Zhejiang, China. The document does not explicitly state if the data was retrospective or prospective, but clinical studies (user evaluations) generally imply prospective data collection in a controlled environment.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

This information is not provided in the document. For blood glucose monitoring systems, the ground truth is typically established using a laboratory reference method (e.g., YSI analyzer), rather than expert consensus on interpretation.

4. Adjudication Method for the Test Set

This information is not provided in the document. Given that the ground truth for blood glucose is typically a laboratory reference measurement, adjudication by experts wouldn't be directly applicable in the same way it would be for image-based diagnostics.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

This is not applicable to a Blood Glucose Monitoring System. MRMC studies are relevant for AI in diagnostic imaging where human readers interpret cases. This device is a direct measurement system.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

The device itself is a standalone system for glucose measurement. The "study" here refers to the overall performance of the device in the hands of the intended users. The clinical studies (user evaluations) assess the device's performance when used by individuals (humans in the loop). The "algorithm only" concept doesn't apply as it's a physical meter and test strip system.

7. The Type of Ground Truth Used

While not explicitly stated for all studies, for blood glucose monitoring systems, the ground truth for accuracy studies is typically established using a laboratory reference method (e.g., YSI Glucose Analyzer) on venous blood samples. The document implies this by stating that clinical performance met FDA SMBG OTC Guidance, which mandates comparison to such reference methods.

8. The Sample Size for the Training Set

This information is not provided. For a physical device like a blood glucose meter, there isn't a "training set" in the same sense as machine learning algorithms. The device's calibration and performance characteristics are established during its design and manufacturing process, and then validated through laboratory and clinical studies.

9. How the Ground Truth for the Training Set Was Established

Not applicable as there is no traditional "training set" for an AI algorithm here. The performance is validated against established reference methods and clinical guidelines.

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