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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, and of Type 2 diabetes mellitus in persons 18 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, and of Type 2 diabetes mellitus in persons 18 years of age and older requiring insulin.

Predictive Low Glucose technology is intended for single patient use and requires a prescription.

SmartGuard technology

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, ages 7 years or older, and by people with Type 2 diabetes, ages 18 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

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, ages 7 years or older, and by people with Type 2 diabetes, ages 18 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 summary concern the Medtronic SmartGuard Technology (Advanced Hybrid Closed Loop algorithm, AHCL) and Predictive Low Glucose Technology (Predictive Low Glucose Management algorithm, PLGM). The document details the devices' descriptions, indications for use, comparison to predicate devices, and summaries of non-clinical and clinical performance data.

However, it's important to note that this document primarily focuses on establishing substantial equivalence to previously cleared predicate devices and does not explicitly state specific acceptance criteria (performance targets) for clinical efficacy metrics (e.g., specific HbA1c reduction percentages or time in range targets needed for clearance) and does not present the study results in a direct "acceptance criteria vs. reported performance" table format for those specific targets. Instead, it highlights that the clinical data "confirmed the safety and effectiveness" and "demonstrated improved glycemic outcomes" or "non-inferiority," and that "the results also confirm that use...was associated with improved glucose control."

The document also provides details about the clinical studies without explicitly labelling them as "the study that proves the device meets the acceptance criteria" in the way a clinical trial protocol would specify primary and secondary endpoints and their statistical targets. Instead, it justifies substantial equivalence through the provided study data.

Therefore, the response below will extract the most relevant information based on your request, presenting the outcomes demonstrated by the studies as "reported device performance" where specific metrics are given, and noting the absence of explicit, pre-defined acceptance criteria targets in the clearance letter itself.

Acceptance Criteria and Study to Prove Device Meets Criteria

The FDA 510(k) summary for Medtronic's SmartGuard Technology and Predictive Low Glucose Technology establishes substantial equivalence to predicate devices. While the document asserts the safety and effectiveness, it does not explicitly define quantitative "acceptance criteria" for specific performance metrics in a pass/fail sense within this summary. Instead, it relies on demonstrating improved or non-inferior clinical outcomes compared to baseline or predicate performance. The clinical studies described confirm the safety and effectiveness and demonstrate associations with improved glucose control, which implicitly means the performance was deemed acceptable by the FDA for substantial equivalence.

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

1. Table of Acceptance Criteria and Reported Device Performance

As explicit quantitative acceptance criteria (e.g., "HbA1c must reduce by X%") are not stated in this 510(k) summary, the table below presents the demonstrated clinical outcomes as "Reported Device Performance" highlighting the positive findings that supported clearance.

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

The primary "test sets" for clinical effectiveness and safety were the patient cohorts in the described clinical studies.

• AHCL with Simplera Sync CGM (Type 1 Diabetes): The sample size details for this study (originally in K251217) are not explicitly provided in this 510(k) summary. However, it confirmed safety and effectiveness in Type 1 Diabetes patients.
• AHCL with Simplera Sync CGM & Guardian 4 CGM (Type 2 Diabetes) (P160017/S124):
  • Phase 1 (Guardian 4 CGM): N = 95 subjects.
  • Phase 2 (Simplera Sync sensor): N = 302 subjects (66 "transition", 236 "naive").
  • Data Provenance: Multi-center, single-arm study. The document does not specify countries but implies clinical trial settings. Given the general nature of FDA submissions, it would typically involve US-based and possibly international sites. Clinical data is generally considered prospective for such trials.
• AHCL with Guardian 4 CGM and Lyumjev Insulin (Type 1 Diabetes) (P160017/S125):
  • ITT Population: N = 101 (Age 7-17 Years), N = 110 (Age 18-80 Years).
  • Data Provenance: Single-arm, multi-center, home clinical investigation. The document does not specify countries but implies clinical trial settings. Prospective.
• AHCL with Guardian 4 CGM and Fiasp Insulin (Type 1 Diabetes) (P160017/S125):
  • ITT Population: N = 107 (Age 7-17 Years), N = 116 (Age 18-80 Years).
  • Data Provenance: Global multi-center, single-arm study. Countries mentioned: Australia, Canada, United States. Prospective.
• PLGM Algorithm: Evaluated for safety in a multi-center, single-arm, in-clinic study of the MiniMed 640G System. The sample size for this study (originally in K251217) is not explicitly provided in this 510(k) summary.

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

The document refers to "clinical data" and "multi-center, single-arm studies" involving patients. For automated glycemic control devices, the "ground truth" for glucose values is typically established by laboratory reference methods (e.g., YSI glucose analyzer) performed by trained clinical staff as part of the study protocol, not by experts determining a ground truth in the interpretative sense. The efficacy endpoints (HbA1c, Time in Range, hypoglycemia) are derived from objective measurements, not subjective expert assessment of an image or signal.

There is no mention of "experts" in the context of establishing ground truth for glucose values or clinical outcomes. Clinical trials are monitored by clinical investigators (physicians, endocrinologists) and their teams, who ensure data integrity and protocol adherence, but they are not establishing a "ground truth" in the way radiologists might for AI image analysis.

4. Adjudication Method for the Test Set

Adjudication methods (e.g., 2+1, 3+1) are typically used in studies where subjective interpretation is involved, such as in reading medical images. For automated glycemic control devices, clinical outcomes are based on objective measurements (e.g., sensor glucose, lab-measured HbA1c). Therefore, the concept of an adjudication method as described does not directly apply to the clinical performance data presented here. Device-related adverse events would be reported and reviewed by the study investigators and likely an independent Data Safety Monitoring Board (DSMB), but this isn't an "adjudication method" for the primary clinical endpoints. The document does not explicitly describe an adjudication method for the objective clinical endpoints.

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

No, an MRMC comparative effectiveness study was not done. MRMC studies are typically used to evaluate the performance of diagnostic devices or AI algorithms that assist human readers (e.g., radiologists interpreting images). The SmartGuard and PLGM technologies are automated insulin delivery algorithms, not diagnostic tools that human readers interpret. Therefore, the concept of "how much human readers improve with AI vs without AI assistance" does not apply here. The studies evaluate the algorithm's direct impact on glycemic control in patients.

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

Yes, the core of the evaluation involves the "standalone" or "algorithm-only" performance in controlling glucose levels. The AHCL algorithm automatically adjusts basal insulin delivery and delivers correction boluses. The PLGM algorithm automatically suspends insulin delivery. While these systems require user input for meal boluses and setup, and interaction with alerts/alarms, the "SmartGuard technology" and "Predictive Low Glucose technology" themselves are software algorithms that function autonomously based on sensor glucose values to manage insulin delivery. The clinical studies assess the efficacy and safety of these algorithms in action within the device system (pump + CGM).

7. Type of Ground Truth Used

The ground truth for evaluating device performance in these studies is based on objective clinical measurements and patient outcomes. Specifically:

• Continuous Glucose Monitoring (CGM) sensor values: These are the primary input to the algorithm. While not explicitly stated as the ground truth for the algorithm's inputs, the accuracy of these values is critical and would have been established independently for the compatible CGMs.
• Laboratory-measured HbA1c: A standard clinical biomarker for average blood glucose over 2-3 months.
• Time-in-Range (TIR): Percentage of time spent with sensor glucose values within a target range (e.g., 70-180 mg/dL), derived from CGM data.
• Time-below-range (TBR): Percentage of time spent with sensor glucose values below a predefined threshold (e.g., <70 mg/dL), derived from CGM data.
• Adverse Events (AEs) and Serious Adverse Events (SAEs): Collected during clinical trials to assess safety.

These are physiological and clinical outcome data, not expert consensus or pathology reports in the typical sense.

8. The Sample Size for the Training Set

The document does not provide details on the sample size used for training the algorithm. This 510(k) summary focuses on the clinical data for validation of the finalized algorithms. The training set would be data used during the development phase of the algorithms, which is typically proprietary and not disclosed in 510(k) summaries. It would likely involve a large dataset of glucose profiles and insulin delivery patterns.

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

Similarly, the document does not describe how the ground truth for the training set was established. For algorithms predicting glucose or controlling insulin, development and training would typically rely on:

• Retrospective or prospective real-world glucose and insulin data: Collected from individuals with diabetes using CGMs and insulin pumps, potentially under controlled conditions or in free-living settings.
• Validated glucose measurements: Such as YSI or other reference laboratory methods for blood glucose, and accurate CGM data.
• Clinical expert knowledge: Incorporating understanding of diabetes physiology, insulin pharmacokinetics/pharmacodynamics, and desired glycemic targets.
• Mathematical models of glucose metabolism: To simulate physiological responses and generate synthetic data for training.

The ground truth would be the actual glucose values and the physiological responses to insulin delivery, enabling the algorithm to learn patterns and predict future glucose trends or optimal insulin dosing.

Ask a specific question about this device

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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