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The Bigfoot Unity® Diabetes Management System is indicated for the management of diabetes in persons age 12 years and older.

Bigfoot Unity® provides glucose monitoring data via the Abbott FreeStyle Libre 2 Flash Glucose Monitoring sensor. The system incorporates real time alarm capabilities and is designed to replace blood glucose testing for diabetes treatment decisions, unless otherwise indicated. The device is intended to provide insulin dose information using the available glucose data to assist persons with diabetes mellitus who use disposable pen-injectors for the self-injection of insulin in implementing health care provider recommended insulin dose regimens. The device is intended for single patient use only and requires a prescription.

Bigfoot Unity® is also intended to communicate autonomously with digitally connected medical devices where the user manually controls therapy decisions.

Bigfoot Unity Diabetes Management System ("Bigfoot Unity System") integrates The continuous glucose monitoring with insulin dose recommendations to support people with diabetes mellitus who use disposable insulin pens for self-injection of insulin. The system consists of the Abbott Diabetes Care, Inc. FreeStyle Libre 2 Flash Glucose Monitoring system ("FreeStyle Libre 2") integrated continuous glucose monitor (iCGM) sensor, two reusable insulin pen caps (one each for rapid-acting and long-acting insulin pens) and a mobile application. The components communicate via near field communication (NFC) and Bluetooth.

The device generates glucose data using the FreeStyle Libre 2 sensor and displays the data (value and trend) on the rapid-acting insulin pen cap. The rapid-acting pen cap also displays correction and meal insulin doses based upon settings prescribed by the user's healthcare provider and the available glucose data. The long-acting pen cap displays the long-acting insulin dose prescribed by the user's healthcare provider. From the dose recommendations on the pen caps as well as other contextually relevant information such as glucose trend arrows and current exercise status, users determine the doses to take. Users manually select an insulin dose and administer it using the pens according to the insulin manufacturers' instructions. In addition to dose information, both pen caps track the time of insulin doses.

The mobile app provides fixed and configurable system alerts based upon data generated by the FreeStyle Libre 2 sensor. It also enables entry of the healthcare provider prescribed insulin dosing regimen as well as provides system alerts and historical information. In addition, the mobile app manages the secure wireless communication between the system components and enables the transfer of the system data to the cloud.

The provided text does not contain specific acceptance criteria, reported device performance metrics, or detailed study results for the Bigfoot Unity® Diabetes Management System in the format requested. The document is a 510(k) summary, which typically provides an overview rather than granular study details.

However, based on the information provided, here's what can be extracted and inferred about the studies conducted:

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

The document states: "In all instances, the Bigfoot Unity System functioned as intended and the results of the testing met the acceptance criteria." and "Results of the software executed protocols for the Unity System met the acceptance criteria and therefore support that the Bigfoot Unity software is acceptable for its intended use."

The specific numerical acceptance criteria and corresponding reported device performance metrics (e.g., accuracy percentages, error rates) are not detailed in this summary. The summary refers to the compliance with various standards and successful completion of tests.

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

• Software Verification and Validation: Not specified.
• Human Factors: Not specified.
• Electromagnetic Compatibility and Electrical Safety: Not specified.
• Cybersecurity: Not specified.
• Analytical and Clinical Performance: "The following performance characteristics were established for the predicate Bigfoot Unity System in K202145 and are not impacted by the modifications leading to the subject device." This implies that the clinical performance data comes from the predicate device (K202145), but the sample size and provenance of that predicate study are not detailed in this document.

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 document. Ground truth establishment, if applicable to the studies mentioned (e.g., analytical and clinical performance from the predicate), is not described.

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

This information is not provided in the document.

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:

The document describes the device as providing "insulin dose information using the available glucose data to assist persons with diabetes mellitus" and "dose recommendations based on glucose information where the user manually controls actions for therapy decisions." While it assists users, there is no mention of a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers (users) with and without AI assistance for this device. The focus is on the system providing recommendations based on glucose data.

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

The software verification and validation section indicates that standalone algorithm performance was evaluated: "Verification activities included unit, component, system integration, and system level testing which verified functionality of the device against established software requirements." This would implicitly involve algorithm-only testing to ensure it meets specifications before assessing human interaction.

7. The type of ground truth used:

• For software verification and validation: "established software requirements" serve as the ground truth.
• For human factors: "safe and effective use of the device by the intended user groups" and compliance with "FDA Guidance, Applying Human Factors and Usability Engineering to Medical Devices (2016) and ANSI/AAMI/IEC 62366" serve as ground truth or benchmarks.
• For electromagnetic compatibility and electrical safety: Compliance with "IEC 60601-1:2005", "IEC/EN 60601-1-2:2014", "IEC CISPR 11", "IEC 60601-1-11:2015", and FDA guidance for "Radio Frequency Wireless Technology in Medical Devices" are the ground truths.
• For cybersecurity: "analysis of confidentiality, integrity, and availability for data, information and software" with "appropriate risk mitigation controls" being the ground truth.
• For analytical and clinical performance: This refers to the predicate device (K202145). The specific type of ground truth (e.g., lab reference values, clinical outcomes) for the predicate's analytical and clinical performance is not described in this document.

8. The sample size for the training set:

This information is not provided in the document. The document describes verification and validation rather than the development or training of AI models.

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

This information is not provided in the document, as details on a training set or its associated ground truth establishment are absent.

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The FreeStyle Libre 2 Flash Glucose Monitoring System is a continuous glucose monitoring (CGM) device with real time alarms capability indicated for the management of diabetes in persons age 4 and older. It is intended to replace blood glucose testing for diabetes treatment decisions, unless otherwise indicated.

The System also detects trends and tracks patterns and aids in the detection of episodes of hyperglycemia and hypoglycemia, facilitating both acute and long-term therapy adjustments. Interpretation of the System readings should be based on the glucose trends and several sequential readings over time.

The System is also intended to autonomously communicate with digitally connected devices. The System can be used alone or in conjunction with these digitally connected devices where the user manually controls actions for therapy decisions.

The FreeStyle Libre 3 Continuous Glucose Monitoring System is a real time continuous glucose monitoring (CGM) device with alarms capability indicated for the management of diabetes age 4 and older. It is intended to replace blood glucose testing for diabetes treatment decisions, unless otherwise indicated.

The System also detects trends and tracks patterns and aids in the detection of episodes of hyperglycemia and hypoglycemia, facilitating both acute and long-term therapy adjustments. Interpretation of the System readings should be based on the glucose trends and several sequential readings over time.

The System is also intended to autonomously communicate with digitally connected devices. The System can be used alone or in conjunction with these digitally connected devices where the user manually controls actions for therapy decisions.

The FreeStyle Libre 2 and FreeStyle Libre 3 are integrated continuous glucose monitoring (iCGM) Systems designed to be used alone or in conjunction with digitally connected devices. The FreeStyle Libre 2 System consists of a Sensor and either a Reader or the FreeStyle Libre 2 App downloaded to a compatible smartphone as a primary display device. The FreeStyle Libre 3 System consists of a Sensor and the FreeStyle Libre 3 App downloaded to a compatible smartphone as a primary display device. Both Systems can communicate glucose data and other information wirelessly and securely to and from these digitally connected devices as described below:

• Wireless communication from the FreeStyle Libre 2 Sensor or FreeStyle Libre 3 Sensor directly to an interoperable receiver device, which connects with the Sensor using the near field communication (NFC) and Bluetooth Low Energy wireless interfaces provided by the Sensor
• The FreeStyle Libre 2 App or FreeStyle Libre 3 App communicates through the cloud to another software device, such as LibreView.

Compared to the respective predicate devices, the proposed subject devices include an additional software component, the Libre Data Sharing API. The Libre Data Sharing API is a cloud-based application programming interface (API) that enables communication of glucose data including alarms through the cloud from the FreeStyle Libre 2 System or FreeStyle Libre 3 System to authorized client software on digitally connected devices. The data transmitted by the API to authorized client software can be used for specific and permitted use cases, including nonmedical device applications, medical device data analysis, CGM secondary display alarm, active patient monitoring, and treatment decisions. Use of the Libre Data Sharing API and the CGM information it transmits is limited by the indications for use of the iCGM systems with which it is used.

The Libre Data Sharing API does not have any command or control over the client software, nor does it allow for the client software to have any command or control over the FreeStyle Libre 2 or FreeStyle Libre 3 Systems. Additionally, glucose data and alarms from the connected iCGM system are not modified or manipulated by the Libre Data Sharing API through its transmission to the authorized client software.

The display device of the connected FreeStyle Libre 2 or FreeStyle Libre 3 Systems, which directly receives the data from the Sensor, continues to serve as a primary display device for the glucose data and alarms. The current components of the FreeStyle Libre 2 and FreeStyle Libre 3 Systems (sensor/applicator and primary display devices) have not been modified as a result of the added the Libre Data Sharing API.

This document is a 510(k) Summary for the FreeStyle Libre 2 Flash Glucose Monitoring System and FreeStyle Libre 3 Continuous Glucose Monitoring System. It describes the devices and argues for their substantial equivalence to previously cleared predicate devices.

Here's an analysis of the acceptance criteria and study information provided in the document:

1. Acceptance Criteria and Reported Device Performance:

The document doesn't present a table of numerical acceptance criteria with corresponding performance statistics. Instead, it argues for substantial equivalence based on the technological characteristics and intended use being the same as the predicate devices, with the addition of the Libre Data Sharing API.

The key acceptance criteria, implicitly, are that the devices continue to perform according to specifications and meet their technological and performance criteria, as demonstrated by verification and validation, aligning with the predicate devices' cleared performance.

2. Sample Size and Data Provenance:

The document states: "The proposed subject devices with the Libre Data Sharing API were verified and validated according to ADC's internal design control process and in accordance with the applicable special controls for integrated continuous glucose monitoring systems. The testing demonstrated that the subject devices conform to the iCGM special controls per 21 CFR 862.1355 and that they performed according to specifications and met their technological and performance criteria."

• Sample Size: The document does not specify the sample sizes used for the test set or the training set. It refers broadly to "performance testing" and "data provided in this pre-market notification."
• Data Provenance: The document does not explicitly state the country of origin or whether the studies were retrospective or prospective. Given it's a 510(k) submission for a medical device, it's highly likely that the underlying performance data comes from clinical studies, often multi-site and prospective, but this document does not detail them. The focus of this 510(k) is specifically on the added software component (Libre Data Sharing API) and its impact on substantial equivalence, rather than re-proving the core glucose monitoring accuracy which would have been established in previous 510(k) clearances.

3. Number of Experts and Qualifications for Ground Truth:

The document concerns the addition of a data sharing API to existing glucose monitoring systems. It does not mention the use of experts to establish ground truth in the context of a reader study for the new component. The primary ground truth for glucose monitoring devices is typically derived from lab reference methods (e.g., YSI analyzer for blood glucose), rather than expert adjudication of images or clinical assessments.

4. Adjudication Method for the Test Set:

Not applicable in the context of this submission. The submission is about an API for device data sharing, not a diagnostic imaging or clinical assessment device requiring expert adjudication.

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

No, an MRMC study was not conducted or mentioned. This type of study is relevant for AI-assisted diagnostic tools that impact human reader performance, typically in radiology or pathology. This submission is for a continuous glucose monitoring system and an API to share its data.

6. Standalone (Algorithm Only) Performance:

The document states: "The Libre Data Sharing API does not have any command or control over the client software, nor does it allow for the client software to have any command or control over the FreeStyle Libre 2 and FreeStyle Libre 3 Systems. Additionally, glucose data and alarms from the FreeStyle Libre 2 and FreeStyle Libre 3 Systems are not modified or manipulated by the Libre Data Sharing API through its secured transmission to the authorized client software."

This implies that the core algorithm performance (i.e., glucose measurement accuracy) of the FreeStyle Libre 2 and 3 systems, which operates independently of the Libre Data Sharing API, has already been established in previous clearances (K210943, K213996, K212132). This submission focuses on the safety and effectiveness of the data sharing mechanism, not the standalone performance of the glucose measurement algorithms themselves. The API itself is primarily a data transmission layer, not a
diagnostic algorithm.

7. Type of Ground Truth Used:

For the underlying glucose monitoring systems (FreeStyle Libre 2 and 3), the ground truth for performance studies is typically laboratory reference measurements (e.g., plasma glucose values obtained from a YSI glucose analyzer) against which the accuracy of the interstitial glucose readings are compared. The document does not specifically detail how the ground truth was established for the "performance testing" related to the API, as the API's function is data transmission, not direct measurement. The ground truth for the API's performance would likely focus on the integrity, security, and timeliness of data transfer.

8. Sample Size for the Training Set:

The document does not specify the sample size for any training set. This submission is for a device modification (adding an API), not a de novo AI/ML algorithm that typically undergoes distinct training and test phases. The core glucose algorithms were already developed and cleared.

9. How the Ground Truth for the Training Set was Established:

Not applicable, as this submission pertains to an API for data sharing and refers to "verification and validation" for "performance testing" of the modified device, rather than the development and training of a new algorithm requiring a specific ground truth establishment for a training set. The underlying glucose measurement algorithms would have had their own ground truth established during their prior development and clearance, likely using reference lab methods.

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The FreeStyle Libre 3 Continuous Glucose Monitoring System is a real time continuous glucose monitoring (CGM) device with alarms capability indicated for the management of diabetes in persons age 4 and older. It is intended to replace blood glucose testing for diabetes treatment decisions, unless otherwise indicated.

The System also detects trends and tracks patterns and aids in the detection of episodes of hyperglycemia and hypoglycemia, facilitating both acute and long-term therapy adjustments. Interpretation of the System readings should be based on the glucose trends and several sequential readings over time.

The System is also intended to autonomously communicate with digitally connected devices. The System can be used alone or in conjunction with these digitally connected devices where the user manually controls actions for therapy decisions.

The FreeStyle Libre 3 Continuous Glucose Monitoring System (herein referred to as the 'FreeStyle Libre 3 System' or 'System') is an integrated continuous glucose monitoring system (iCGM) that provides real time continuous glucose measurements every minute to provide glucose levels, trends, and alarms. The System requires a prescription and is intended for home use. The System consists of the following components: a Sensor which transmits via Bluetooth Low Energy (BLE), and a mobile application, FreeStyle Libre 3 App, downloaded to a compatible smartphone running iOS operating system. The FreeStyle Libre 3 System provides the user with real-time glucose measurements (glucose values) accompanied by trend information (glucose arrows) and historical glucose information (glucose graph). The user may make treatment decisions based in part on the Sensor glucose results provided by the System. The System also provides fixed and configurable alarms designed to warn the user of Low Glucose, High Glucose, or Signal Loss.

Here's a breakdown of the acceptance criteria and study information for the FreeStyle Libre 3 Continuous Glucose Monitoring System, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't provide a specific table of numerical acceptance criteria for clinical performance (like MARD values with defined thresholds). Instead, it states that the device "met the iCGM special controls requirements per 21 CFR 862.1355." However, it does list several performance characteristics that were "confirmed to support substantial equivalence," implying that specific acceptance criteria were met for each. Without the actual criteria for each, here's a general table based on the information provided:

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

• Sample Size: The document mentions "a bridging clinical study" but does not explicitly state the number of subjects or data points used in this study.
• Data Provenance: Not explicitly stated (e.g., country of origin, retrospective or prospective). The phrasing "bridging clinical study" generally implies a prospective study designed to bridge results from a previous study or device.

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 glucose monitoring systems, ground truth is typically established using a highly accurate reference method (e.g., YSI glucose analyzer) rather than expert consensus on images.

4. Adjudication Method for the Test Set

This information is not provided in the document.

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 MRMC study was mentioned. The FreeStyle Libre 3 is a Continuous Glucose Monitoring (CGM) system, which provides direct glucose readings to the user, not an AI system that assists human "readers" in interpreting diagnostic images or data where MRMC studies are typically performed. The device is intended to replace blood glucose testing for treatment decisions, not to aid human interpretation of complex medical cases.

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

• Yes, a standalone performance assessment was done. The document states, "The subject device calculates glucose information identically to the predicate device, and the combined System accuracy of the FreeStyle Libre 3 System and FreeStyle Libre 2 System met the iCGM special controls requirements per 21 CFR 862.1355." This refers to the accuracy of the algorithm itself in determining glucose values from the sensor data. The "clinical performance" testing directly assesses the device's accuracy without human intervention influencing the glucose measurement.

7. The Type of Ground Truth Used

• Reference Glucose Methods: While not explicitly named, for CGM devices, the standard ground truth for clinical accuracy studies is typically venous blood samples analyzed by a laboratory reference method, such as a YSI glucose analyzer. The document states accuracy was demonstrated, which implies comparison to such a reference.

8. The Sample Size for the Training Set

• Not provided. The document describes a "bridging clinical study" for performance validation but does not detail the size of any training sets used for algorithm development. It does state that the "Sensor Glucose Algorithm established for the predicate device" is the same, implying the algorithm's core might have been trained previously for the FreeStyle Libre 2.

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

• Not provided. Given that the subject device uses the "ADC Glucose Algorithm established for the predicate device," the ground truth establishment for any original training would have likely involved comparisons to laboratory reference glucose measurements in clinical studies. However, details of such a training process or ground truth for a training set are not in this document.

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The FreeStyle Libre 3 Continuous Glucose Monitoring System is a real time continuous glucose monitoring (CGM) device with alarms capability indicated for the management of diabetes in persons age 4 and older. It is intended to replace blood glucose testing for diabetes treatment decisions, unless otherwise indicated.

The System also detects trends and tracks patterns and aids in the detection of episodes of hyperglycemia and hypoglycemia, facilitating both acute and long-term therapy adjustments. Interpretation of the System readings should be based on the glucose trends and several sequential readings over time.

The System is also intended to autonomously communicate with digitally connected devices. The System can be used alone or in conjunction with these digitally connected devices where the user manually controls actions for therapy decisions.

The FreeStyle Libre 3 Continuous Glucose Monitoring System (herein referred to as the 'FreeStyle Libre 3 System' or 'System') is an integrated continuous glucose monitoring system (iCGM) that provides real time continuous glucose measurements every minute to provide glucose levels, trends, and alarms. The System requires a prescription and is intended for home use. The System consists of the following components: a Sensor which transmits via Bluetooth Low Energy (BLE), and a mobile application, FreeStyle Libre 3 App, downloaded to a compatible smartphone running on Android operating system. The FreeStyle Libre 3 System provides the user with real-time glucose measurements (glucose values) accompanied by trend information (glucose arrows) and historical glucose information (glucose graph). The user may make treatment decisions based in part on the Sensor glucose results provided by the System. The System also provides fixed and configurable alarms designed to warn the user of Low Glucose, High Glucose, or Signal Loss.

The document details the FreeStyle Libre 3 Continuous Glucose Monitoring System and its performance testing to support substantial equivalence to its predicate device, the FreeStyle Libre 2 Flash Glucose Monitoring System.

Here's an analysis of the acceptance criteria and study information provided:

1. Table of Acceptance Criteria and Reported Device Performance

The document states that a bridging clinical study was conducted, and the "combined System accuracy of the FreeStyle Libre 3 System and FreeStyle Libre 2 System met the iCGM special controls requirements per 21 CFR 862.1355." However, specific numerical acceptance criteria (e.g., MARD percentage, accuracy at different glucose ranges) and the exact reported performance of the FreeStyle Libre 3 System are not explicitly listed in a detailed table within the provided text segments. Instead, a general statement of meeting the iCGM special controls is given.

To fill this, we must infer that the implicit acceptance criteria are those defined by the iCGM special controls (21 CFR 862.1355) for integrated continuous glucose monitoring systems. These typically include accuracy metrics like MARD (Mean Absolute Relative Difference) against a reference method, as well as performance across different glucose ranges (hypoglycemic, euglycemic, hyperglycemic). Without the actual study report, we cannot provide specific numbers for the FreeStyle Libre 3's performance or exact acceptance criteria.

General Interpretation of iCGM Special Controls (Implicit Acceptance Criteria):
iCGM special controls generally require demonstration of:

• Accuracy: Sufficient accuracy across the glucose range (e.g., MARD values below a certain threshold when compared to a laboratory reference).
• Safety: Acceptable safety profile with minimal adverse events.
• Performance in various conditions: Performance evaluated across different patient populations, wear sites, and duration of use.
• Alarm performance: Accuracy and reliability of hypoglycemia and hyperglycemia alarms.
• Interoperability: Functionality with digitally connected devices.

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

The document states: "ADC conducted a bridging clinical study to demonstrate comparability of the performance of the FreeStyle Libre 3 System to the predicate FreeStyle Libre 2 System, cleared under K210943."

• Sample Size for Test Set: Not explicitly stated in the provided text.
• Data Provenance: Not explicitly stated in the provided text (e.g., country of origin, retrospective or prospective). It is implied to be prospective as it's a clinical study for FDA clearance.

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 text. For CGM devices, the "ground truth" (reference glucose values) is typically established using a highly accurate laboratory reference method (e.g., YSI glucose analyzer) on blood samples, not expert consensus interpreting images or clinical cases. Therefore, the concept of "experts establishing ground truth" in the way it applies to image interpretation is not directly applicable here. The ground truth would be the laboratory-measured glucose values.

4. Adjudication Method for the Test Set

The concept of an "adjudication method" (like 2+1 or 3+1) is primarily relevant for studies where multiple human readers interpret data (e.g., radiology images) and a consensus or tie-breaking mechanism is needed for the ground truth. For a CGM device, the ground truth is established by a quantitative, objective measurement (laboratory blood glucose). Therefore, an adjudication method in this context is not applicable/not mentioned as it's not a reader study.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was Done

No, an MRMC comparative effectiveness study was not done. The FreeStyle Libre 3 is a standalone device that measures glucose; it does not involve human readers interpreting its output in a diagnostic context that would warrant an MRMC study. The "comparative effectiveness" mentioned refers to its performance against a predicate device and standard laboratory methods, not a comparison of human reader performance with and without AI assistance.

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

Yes, a standalone study was done. The entire premise of the submission is to demonstrate the performance of the FreeStyle Libre 3 System as a standalone integrated continuous glucose monitoring device. The bridging clinical study, designed to show "comparability of the performance of the FreeStyle Libre 3 System to the predicate FreeStyle Libre 2 System," inherently evaluates the algorithm's performance without direct human intervention in the glucose measurement and calculation process. The FreeStyle Libre 3 delivers "real-time continuous glucose measurements every minute" and its "System accuracy" was evaluated.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

The ground truth for a CGM device is typically a highly accurate laboratory reference method (e.g., YSI glucose analyzer) performed on venous blood samples taken concurrently with the CGM readings. While not explicitly stated, this is the standard for CGM accuracy studies.

8. The Sample Size for the Training Set

The document mentions that the "Sensor Glucose Algorithm established for the predicate device" is the same for the FreeStyle Libre 3. It also states that the FreeStyle Libre 3 contains a "software algorithm for conversion of the raw glucose measurements from the Sensor to calculate glucose results." This implies the algorithm was developed and trained prior to this submission, likely using data from previous FreeStyle Libre sensor generations.

The sample size for the training set is not provided in this document.

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

Given that the "Sensor Glucose Algorithm" is the same as the predicate device, it can be inferred that the ground truth for training this algorithm was established similarly to the predicate device's clearance. This would typically involve pairing sensor readings with laboratory reference blood glucose measurements (e.g., YSI) taken from human subjects during previous clinical studies. The specific details of how this ground truth was established for the training set are not provided in the given text.

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The FreeStyle Libre 2 Flash Glucose Monitoring System is a continuous glucose monitoring (CGM) device with real time alarms capability indicated for the management of diabetes in persons age 4 and older. It is intended to replace blood glucose testing for diabetes treatment decisions, unless otherwise indicated.

The System also detects trends and tracks patterns and aids in the detection of episodes of hyperglycemia and hypoglycemia, facilitating both acute and long-term therapy adjustments. Interpretation of the System readings should be based on the glucose trends and several sequential readings over time.

The System is also intended to autonomously communicate with digitally connected devices. The System can be used alone or in conjunction with these digitally connected devices where the user manually controls actions for therapy decisions.

The System can be used with the FreeStyle Libre 2 Sensor (14 day) or the FreeStyle Libre 2 MediRx Sensor (10 day).

The FreeStyle Libre 2 Flash Glucose Monitoring System with the FreeStyle Libre 2 App -Android (herein referred to as the 'FreeStyle Libre 2 System' or 'System') is an integrated continuous glucose monitoring system (iCGM) that provides continuous glucose measurements every minute to provide glucose levels, trends, and real time alarms capability to aid in the management of diabetes. The System requires a prescription and is intended for home use. The System consists of the following components: a Sensor which transmits yia Bluetooth Low Energy (BLE), a BLE enabled display device (Reader), and an Android or iOS mobile app (FreeStyle Libre 2 App) downloaded to a compatible smartphone. Scanning of the Sensor via Reader or App provides the user with real-time glucose measurements (glucose values) accompanied by trend information (glucose arrows) and historical glucose information (glucose graph). The user may make treatment decisions based in part on the Sensor glucose results provided by the System. The System also provides configurable alarms designed to warn the user of Low Glucose, High Glucose or Signal Loss.

This 510(k) summary describes the FreeStyle Libre 2 Flash Glucose Monitoring System (with FreeStyle Libre 2 App), focusing on the addition of an Android application to the existing system. The document states that the Android app utilizes the identical algorithm and implements the same wireless interfaces with the sensor as the previously cleared Reader and iOS app. Therefore, no additional clinical data was used in this 510(k) to support a determination of substantial equivalence for clinical performance. The original clinical performance data was established in predicate devices K193371 and K211102.

Since this 510(k) relies on the clinical performance data established in prior submissions (K193371 and K211102) and explicitly states that no new clinical performance data was generated for this specific submission (K210943) regarding the Android App, the detailed acceptance criteria and study information for clinical accuracy are not directly present within this provided text for K210943. The document focuses on non-clinical performance testing for the Android app.

However, based on the information provided regarding the non-clinical performance of the Android App within this specific 510(k) (K210943):

Table of Acceptance Criteria and Reported Device Performance (Non-Clinical)

Due to the nature of this 510(k) being primarily for the addition of the Android app, the following information regarding clinical performance criteria, sample sizes, expert involvement, and ground truth would have been established in the predicate device studies (K193371 and K211102) and are not detailed in this document (K210943):

• Sample size used for the test set and the data provenance: Not specified in K210943, as clinical data was referenced from predicate devices. For iCGM, clinical studies typically involve a significant number of participants (e.g., hundreds) across varied demographics to assess accuracy across the glycemic range. Data provenance would likely be from multi-center prospective clinical trials conducted in various regions.
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not specified in K210943. In iCGM clinical trials, ground truth is usually established by highly precise laboratory reference methods (e.g., YSI glucose analyzer) rather than expert consensus on device readings.
• Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable for iCGM clinical accuracy studies where the "ground truth" is typically a laboratory reference method.
• 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 for iCGM clinical accuracy studies as described. The device provides direct glucose values, not interpretations requiring human reader assistance in the same way as imaging diagnostics. It replaces blood glucose testing for treatment decisions.
• If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Yes, the core accuracy of the algorithm is evaluated in a standalone manner against laboratory reference values in the predicate device studies. The FreeStyle Libre 2 System itself, including the sensor and its algorithm, performs autonomously to produce glucose readings.
• The type of ground truth used (expert consensus, pathology, outcomes data, etc): For iCGM devices, the gold standard ground truth for accuracy studies is typically laboratory reference methods, such as those performed by a YSI glucose analyzer, which measures glucose concentration in blood plasma.
• The sample size for the training set: Not specified in K210943. Training data for the glucose algorithm would have been part of the original development and validation of the FreeStyle Libre 2 system (K193371 and K211102), and would involve a large, diverse dataset of sensor readings and corresponding reference glucose values.
• How the ground truth for the training set was established: Not specified in K210943. Similar to the test set, the ground truth for training would have been established using highly accurate laboratory reference methods (e.g., YSI analyzer) from blood samples collected concurrently with sensor readings.

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The FreeStyle Libre 2 Flash Glucose Monitoring System is a continuous glucose monitoring (CGM) device with real time alarms capability indicated for the management of diabetes in persons age 4 and older. It is intended to replace blood glucose testing for diabetes treatment decisions, unless otherwise indicated.

The System also detects trends and tracks patterns and aids in the detection of episodes of hyperglycemia and hypoglycemia, facilitating both acute and long-term therapy adjustments. Interpretation of the System readings should be based on the glucose trends and several sequential readings over time.

The System is also intended to autonomously communicate with digitally connected devices. The System can be used alone or in conjunction with these digitally connected devices where the user manually controls actions for therapy decisions.

The System can be used with the FreeStyle Libre 2 Sensor (14 day) or the FreeStyle Libre 2 MediRx Sensor (10 day).

The FreeStyle Libre 2 Flash Glucose Monitoring System is an integrated continuous glucose monitoring (iCGM) system that provides continuous glucose measurements every minute to provide glucose levels, trends, and real-time alarms capability to aid in the management of diabetes. The FreeStyle Libre 2 System consists of two primary components: a Sensor that transmits via Bluetooth Low Energy (BLE), and a BLE enabled display device (Reader). User initiated RFID scanning of the Sensor via Reader provides the user with real-time glucose measurements (glucose values) accompanied by trend information (glucose arrows) and historical glucose information (glucose graph). Users may use the Sensor glucose results and information provided by the System in making treatment decisions. The System also provides configurable alarms designed to warn the user of Low Glucose, High Glucose or Signal Loss. The system is intended for single-patient use at home and requires a prescription.

The Sensor is single use, disposable, and powered by a silver oxide battery. The Sensor is provided as two secondary components, Sensor Applicator and Sensor Pack (electron beam sterilized device) which are used to assemble and apply the Sensor to the back of the user's arm. During Sensor application, the sensor tail is inserted about 5.5 millimeters below the surface of the skin through the guidance of a needle. The needle is retracted back into the applicator after insertion, and the Sensor remains attached to the skin with a medical grade adhesive. The Sensor continuously measures glucose concentration in interstitial fluid and has an 8-hour memory capacity. The Sensor is factory calibrated, does not require fingerstick calibration, and can be worn for up to 14 days.

The Reader is a small handheld device that is powered by a lithium-ion rechargeable battery and uses RFID communication to start new Sensors and to scan Sensors to display and record data and uses BLE communication to issue alarms that notify the user to scan his/her sensor when glucose values pass a high or low glucose threshold. The Reader also has a built-in strip port with blood glucose functionality (that is intended to work with the FreeStyle Precision Neo Blood Glucose test strips, cleared under K171941), and a user interface that includes event logging features.

The proposed subject device is a modified FreeStyle Libre 2 Flash Glucose Monitoring System that adds compatibility with the FreeStyle Libre 2 MediRx Sensor, which can be worn for up to 10 days. The FreeStyle Libre 2 MediRx Sensor design is unchanged from that of the predicate FreeStyle Libre 2 Sensor, which remains compatible with the modified System. In addition, the Sensor glucose algorithm and Reader design of the modified System remain unchanged from those of the predicate.

The alternate 10-day wear duration of the FreeStyle Libre 2 MediRx Sensor is achieved by changing a Sensor configuration parameter at manufacturing, which is detected by the predicate Reader to automatically determine the wear duration and accordingly adjust the user interface display of remaining Sensor wear time and ensure the Sensor cannot report data beyond the configured wear duration. In addition, each Sensor type has an end of life parameter, which determines when the Sensor will automatically shut down. This functionality is already built into the Sensor and Reader and was validated as part of previously conducted software validation under K193371.

Other than the differences related to wear duration, the FreeStyle Libre 2 MediRx Sensor is identical to the predicate Sensor, and the predicate Reader functions as intended with either the predicate FreeStyle Libre 2 Sensor (14 day) or FreeStyle Libre 2 MediRx Sensor (10 day).

The provided text is related to a 510(k) premarket notification for the FreeStyle Libre 2 Flash Glucose Monitoring System, specifically for a modification to include compatibility with a 10-day wear sensor (FreeStyle Libre 2 MediRx Sensor).

The document does not describe an AI/ML-based device where the performance is presented in terms of AI metrics (e.g., accuracy, precision, recall, AUC, etc.) or a multi-reader multi-case (MRMC) study. Instead, it concerns a medical device for continuous glucose monitoring (CGM). The "algorithm" mentioned (Sensor Glucose Algorithm) refers to the internal processing of sensor signals to derive glucose values, not an AI/ML model in the context of clinical decision support or image analysis.

Therefore, many of the requested criteria (e.g., sample size for test/training set in AI context, number of experts for ground truth, adjudication method, MRMC study, standalone performance for AI, type of ground truth for AI) are not applicable to this device's submission.

However, I can extract information related to the device's clinical performance evaluation based on the provided text, which supports its substantial equivalence.

Here's a summary of the relevant information:

1. Acceptance Criteria and Device Performance:

The document states: "Clinical data from the adult and pediatric iCGM clinical studies that supported clearance of the predicate device were re-analyzed to show that use of the subject device with the FreeStyle Libre 2 MediRx Sensor for a 10-day wear duration meets the iCGM special controls for clinical performance set forth in 21 CFR 862.1355."

This indicates that the acceptance criteria are based on the iCGM special controls outlined in 21 CFR 862.1355. The document does not provide a specific table of numerical acceptance criteria or reported device performance metrics (e.g., MARD values, clinical accuracy zones) for the FreeStyle Libre 2 MediRx Sensor in this specific 510(k) submission. It relies on the re-analysis of data from the predicate device's clearance.

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

• Sample Size Used for Test Set: Not explicitly stated for this particular 510(k). The clinical performance is based on "re-analyzed" data from "clinical studies that supported clearance of the predicate device (K193371)." To find specific sample sizes, one would need to refer to the original K193371 submission.
• Data Provenance: Not explicitly stated regarding country of origin. The data is based on "clinical studies that supported clearance of the predicate device." It's retrospective in the sense that existing data was re-analyzed for the new sensor configuration.

3. Number of Experts and Qualifications for Ground Truth:

• This question is not applicable in the context of this device. The "ground truth" for a glucose monitoring system is typically a high-accuracy reference method for blood glucose (e.g., YSI analyzer in a controlled lab setting), not expert consensus from radiologists or similar.

4. Adjudication Method for the Test Set:

• Not applicable for this type of device. Adjudication methods like '2+1' or '3+1' are common in image analysis studies where human readers provide interpretations and discrepancies are resolved. This is a continuous glucose monitoring device where performance is measured against reference glucose values.

5. MRMC Comparative Effectiveness Study:

• No, an MRMC comparative effectiveness study was not done as described for AI assistance. This device is a direct-to-patient glucose monitoring system, not an AI-assisted diagnostic tool that human readers would use to improve their performance.

6. Standalone Performance:

• Yes, in essence, standalone performance was done. The device itself provides glucose readings without human interpretation or intervention in the measurement process. The "re-analysis" of clinical data to meet iCGM special controls essentially evaluates this standalone performance of the FreeStyle Libre 2 MediRx Sensor. The output of the device (glucose value) is directly compared against a reference method.

7. Type of Ground Truth Used:

• The ground truth used for glucose monitoring devices is typically central laboratory reference glucose measurements (e.g., from a YSI glucose analyzer) taken from blood samples during clinical studies. The document states a re-analysis of "clinical studies," implying the use of such a reference method from the predicate device's trials.

8. Sample Size for the Training Set:

• Not explicitly stated/applicable in the context of AI/ML training. The "Sensor Glucose Algorithm" mentioned is likely a deterministic or model-based algorithm, not a trainable deep learning model in the sense of a "training set" for AI. If the algorithm involves parameters that were "trained" or optimized, the document does not specify the sample size used for this internal process. The primary evaluation here is of the modified sensor with an unchanged algorithm.

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

• Not explicitly stated/applicable in the context of AI/ML training. As above, the ground truth for any underlying algorithm development would refer to the reference glucose measurements used to build or validate that algorithm, but this is not typically referred to as a "training set" in the AI sense for this type of device. The document explicitly states: "the Sensor glucose algorithm... of the modified System remain unchanged from those of the predicate."

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The FreeStyle Libre 2 Flash Glucose Monitoring System is a continuous glucose monitoring (CGM) device with real time alarms capability indicated for the management of diabetes in persons age 4 and older. It is intended to replace blood glucose testing for diabetes treatment decisions, unless otherwise indicated.

The System also detects trends and tracks patterns and aids in the detection of episodes of hyperglycemia and hypoglycemia, facilitating both acute and long-term therapy adjustments. Interpretation of the System readings should be based on the glucose trends and several sequential readings over time.

The System is also intended to autonomously communicate with digitally connected devices. The System can be used alone or in conjunction with these digitally connected devices where the user manually controls actions for therapy decisions.

The FreeStyle Libre 2 Flash Glucose Monitoring System with the FreeStyle Libre 2 App (herein referred to as the 'FreeStyle Libre 2 System' or 'System') is an integrated continuous glucose monitoring system (iCGM) that provides continuous glucose measurements every minute to provide glucose levels, trends and alerts. The System requires a prescription and is intended for home use. The System consists of the following components: a sensor which transmits via Bluetooth Low Energy (BLE), a BLE enabled display device (Reader), and an iOS mobile app (FreeStyle Libre 2 App) downloaded to a compatible smartphone. Scanning of the sensor via Reader or App provides the user with real-time glucose measurements (glucose values) accompanied by trend information (glucose arrows) and historical glucose information (glucose graph). The user may make treatment decisions based in part on the sensor glucose results provided by the System. The System also provides configurable alarms designed to warn the user of Low Glucose, High Glucose or Signal Loss.

The provided text describes the FreeStyle Libre 2 Flash Glucose Monitoring System (with FreeStyle Libre 2 App) and its substantial equivalence to a predicate device. However, the document primarily focuses on explaining the device, its intended use, comparison with a predicate, and the general types of performance testing conducted to support substantial equivalence for the app component.

Crucially, the acceptance criteria and detailed study results proving the device meets those criteria (specific performance metrics, sample sizes for clinical trials, ground truth establishment, expert adjudication, MRMC studies, etc.) are NOT explicitly present in this excerpt. The document states "no additional clinical data beyond that provided in K193371 was used in this 510(k) to support a determination of substantial equivalence" for clinical performance, indicating reliance on prior submissions for the predicate device. Therefore, a comprehensive answer for the requested points regarding the study that proves the device meets the acceptance criteria cannot be fully extracted from this document alone.

However, based on the information provided, I can infer and answer parts of your request.

Here's an attempt to answer your questions based on the provided text, and where information is missing, I will clearly state that:

Device: FreeStyle Libre 2 Flash Glucose Monitoring System (with FreeStyle Libre 2 App)
Predicate Device: FreeStyle Libre 2 Flash Glucose Monitoring System (K193371)

A Table of Acceptance Criteria and Reported Device Performance:

The document does not provide a specific table of acceptance criteria with corresponding performance metrics. It generally states that "Results of executed protocols met the acceptance criteria" for various tests, but does not quantify these criteria or the results.

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

• Sample Size for Test Set: Not specified in this document. The document states that no additional clinical data was used for this 510(k) beyond what was presented in K193371 (the predicate device's submission). The performance testing described (Software V&V, Human Factors, Wireless Coexistence, Cybersecurity, Electrical Safety/EMC) are generally system-level or component-level tests, not direct patient clinical trials with specific "test sets" in the sense of clinical data.
• Data Provenance: Not specified for the clinical data that supports the predicate device (K193371). For the testing described in this document (Software, Human Factors, etc.), it's implied to be internal testing by Abbott Diabetes Care Inc. and conducted in laboratory environments. The document does not specify if it's retrospective or prospective.

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

• The document does not describe specific "ground truth" establishment for a clinical test set in this submission, as it defers to a previous 510(k) (K193371) for clinical performance data. Therefore, the number and qualifications of experts for establishing clinical ground truth are not provided here.
• For Human Factors testing, "usability requirements" were met, implying expert evaluation or user testing, but the specifics of who established the "ground truth" or expert qualifications are not detailed.

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

• Not applicable/Not specified. Since clinical data specifics are not provided in this submission (deferring to K193371), there's no mention of an adjudication method commonly used in clinical trials for ground truth.

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 device is a Continuous Glucose Monitoring (CGM) system, not an AI-assisted diagnostic imaging device that involves human "readers" interpreting cases. Therefore, an MRMC study is not relevant to this device type.

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

• The document implies that standalone performance of the algorithm was assessed for the core glucose measurement. It states, "The App utilizes the identical algorithm and implements the same wireless interfaces with the Sensor as used by the Reader. As the App calculates glucose information identically to the Reader..." This suggests that the algorithm's performance in converting raw sensor measurements to glucose values was established and relied upon from the predicate device's data (K193371). No specific standalone performance metrics are provided in this document, but the fact that the algorithm is "identical" means its standalone performance is assumed to be the same as the predicate.

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

• For the clinical performance of a CGM, the ground truth typically involves lab-based reference glucose measurements (e.g., YSI analyzer or similar highly accurate methods) from blood samples taken concurrently with sensor readings. However, none of these specifics for ground truth are provided in this document, as it refers back to K193371 for clinical data.

7. The sample size for the training set:

• Not specified. The document does not discuss the training of the glucose algorithm. It merely confirms that the app uses the "identical algorithm" as the predicate device.

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

• Not specified. As the document does not detail the training set, it also does not elaborate on how its ground truth was established.

In summary, this 510(k) summary focuses on demonstrating substantial equivalence for the addition of an app to an already cleared CGM system. It relies heavily on the clinical performance and established ground truth from the predicate device's prior submission (K193371) for the core glucose measurement accuracy. The testing described in this particular document pertains mostly to software validation, human factors, wireless communication, cybersecurity, and electrical safety/EMC, which are specific to the new app component and its interaction with the existing system, rather than new clinical accuracy studies.

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The Bigfoot Unity Diabetes Management System is indicated for the management of diabetes in persons age 12 years and older.

Bigfoot Unity provides glucose monitoring data via the Abbott FreeStyle Libre 2 Flash Glucose Monitoring sensor. The system incorporates real time alarm capabilities and is designed to replace blood glucose testing for diabetes treatment decisions, unless otherwise indicated. The device is intended to provide insulin dose information using the available glucose data to assist persons with diabetes mellitus who use disposable pen-injectors for the self-injection of insulin in implementing health care provider recommended insulin dose regimens. The device is intended for single patient use only and requires a prescription.

Bigfoot Unity is also intended to communicate autonomously with digitally connected medical devices where the user manually controls therapy decisions.

The Bigfoot Unity Diabetes Management System ('Bigfoot Unity System') integrates continuous glucose monitoring with insulin dose recommendations to support people with diabetes mellitus who use disposable insulin pens for self-injection of insulin. The system consists of the Abbott Diabetes Care, Inc.'s FreeStyle Libre 2 Flash Glucose Monitoring System ("FreeStyle Libre 2") integrated continuous glucose monitor (iCGM) sensor, two reusable insulin pen caps (one each for rapid-acting and long-acting insulin pens) and a mobile application. The components communicate via near field communication (NFC) and Bluetooth.

The device generates glucose data using the FreeStyle Libre 2 sensor and displays the data (value and trend) on the rapid-acting insulin pen cap. The rapid-acting pen cap also displays correction and meal insulin doses based upon settings prescribed by the user's healthcare provider and the available glucose data. The long-acting pen cap displays the long-acting insulin dose prescribed by the user's healthcare provider. From the dose recommendations on the pen caps as well as other contextually relevant information such as glucose trend arrows and current exercise status, users determine the doses to take. Users manually select an insulin dose and administer it using the pens according to the insulin manufacturers' instructions. In addition to dose information, both pen caps track the time of insulin doses.

The mobile app provides fixed and configurable system alerts based upon data generated by the FreeStyle Libre 2 sensor. It also enables entry of the healthcare provider prescribed insulin dosing regimen as well as provides system alerts and historical information. In addition, the mobile app manages the secure wireless communication between the system components and enables the transfer of the system data to the cloud.

The Bigfoot Unity Diabetes Management System is an integrated continuous glucose monitor (iCGM) designed to assist individuals with diabetes in managing their insulin doses. It incorporates the Abbott FreeStyle Libre 2 Flash Glucose Monitoring system and provides insulin dose recommendations based on glucose data.

Here's an analysis of the acceptance criteria and supporting studies as described in the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with specific numerical targets and corresponding device performance metrics for the Bigfoot Unity System as a complete system beyond the predicate device's performance. Instead, it states that various tests met their respective acceptance criteria, implying successful performance without detailing those criteria quantitatively for this device.

However, it does reference the analytical and clinical performance of the Abbott FreeStyle Libre 2 Flash Glucose Monitor (K193371), which forms a core component of the Bigfoot Unity System. The acceptance criteria for the integrated system are, in part, based on the successful integration and continued performance of this pre-cleared component and the proper functionality of the new elements (pen caps, mobile app, dose recommendations).

Summary of General Performance Claims:

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

The document does not specify the sample size used for the test set for the Bigfoot Unity System's custom components (e.g., pen caps, mobile app, dose recommendations). It mentions "bench test results," "human factors and usability testing," "software verification and validation testing," and "wireless coexistence and EMC testing," but without numerical details on participants or data points.

For the Abbott FreeStyle Libre 2 Flash Glucose Monitor, its analytical and clinical performance was assessed under K193371. The provenance of this data would be from the studies submitted for that specific 510(k) clearance, which is not detailed in this document. Generally, such studies involve prospective clinical trials to evaluate accuracy against a reference method (e.g., YSI glucose analyzer) in a clinical or home-use setting, typically involving participants from various countries (though often with a significant US cohort).

The data concerning the Bigfoot Unity System's specific new features (dose recommendations, pen caps, mobile app) appears to be from retrospective testing (bench, software V&V, environmental) or prospective limited user testing (human factors) but no details of the number of participants or data origin are provided.

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

This information is not provided for the Bigfoot Unity System's specific components. For the Abbott FreeStyle Libre 2 (the iCGM component), the ground truth for its analytical accuracy would have been established using laboratory reference methods (e.g., YSI glucose analyzers) rather than human experts, with the data then statistically compared.

For the human factors testing, "experts" in usability engineering often oversee such studies, but the document does not specify their number or qualifications. The "ground truth" in human factors is often defined by the successful completion of critical tasks without errors, which is assessed against predefined task flows and safety parameters.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method for any test sets related to the Bigfoot Unity System beyond the inherent statistical comparisons and validation processes for technical performance. Since no expert concensus or clinical outcome adjudication is explicitly mentioned for the new components, it appears that direct performance against established technical specifications or usability metrics was used.

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

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study. This type of study is more common for diagnostic imaging devices where human readers (e.g., radiologists) interpret images with and without AI assistance. The Bigfoot Unity System is an iCGM with dose recommendations, not an imaging device, so an MRMC study would generally not be applicable in this context. There is no information provided regarding the effect size of human readers improving with AI vs. without AI assistance.

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

The document does not explicitly describe a standalone algorithm-only performance study for the Bigfoot Unity System's dose recommendation functionality. However, the software verification and validation activities would have tested the internal logic and calculations of the algorithm in a standalone manner (i.e., verifying that given specific glucose data and patient settings, the algorithm outputs the correct dose recommendation according to its specifications). The output of the dose recommendation, while displayed to the user for manual action, is a direct algorithmic output. The performance of the FreeStyle Libre 2 sensor itself is a standalone performance of the glucose monitoring component.

7. Type of Ground Truth Used

For the glucose monitoring component (FreeStyle Libre 2), the ground truth is typically laboratory reference methods (e.g., YSI glucose analyzers for blood glucose levels).

For the Bigfoot Unity System's new components:

• Software Verification and Validation: Ground truth is established by pre-defined functional specifications and requirements. The software is verified to produce expected outputs given specific inputs.
• Human Factors: Ground truth is established by safety and usability metrics, where critical tasks are expected to be completed without error, and the interface is easily understandable.
• Bench, Environmental, EMC, Biocompatibility: Ground truth is established by international standards and regulatory requirements (e.g., ISO, IEC).

8. Sample Size for the Training Set

The document does not provide information on the sample size for the training set. The Bigfoot Unity System's dose recommendation functionality is based on a pre-programmed algorithm reflecting healthcare provider prescribed insulin dosing regimens, not a machine learning model that requires a "training set" in the conventional sense of supervised learning. The FreeStyle Libre 2 sensor would have undergone calibration and potentially model training (if AI is used for signal processing/accuracy improvements) but that detail is not provided here.

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

As noted above, a "training set" for a machine learning model is not explicitly indicated for the Bigfoot Unity System's new components. The system primarily implements established clinical protocols for insulin dosing. Therefore, the "ground truth" for the dose recommendation logic would be based on clinical guidelines and established medical practice for insulin dosing, as prescribed by a healthcare provider. The software validates that it correctly implements these rules.

For the underlying FreeStyle Libre 2 iCGM, if it uses machine learning, the ground truth for any potential sensor calibration/algorithm training would have been established through comparison to laboratory reference glucose measurements (e.g., from YSI devices) from clinical studies.

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The FreeStyle Libre 2 Flash Glucose Monitoring System is a continuous glucose monitoring (CGM) device with real time alarms capability indicated for the management of diabetes in persons age 4 and older. It is intended to replace blood glucose testing for diabetes treatment decisions, unless otherwise indicated.

The System also detects trends and tracks patterns and aids in the detection of episodes of hyperglycemia and hypoglycemia, facilitating both acute and long-term therapy adjustments. Interpretation of the System readings should be based on the glucose trends and several sequential readings over time.

The System is also intended to autonomously communicate with digitally connected devices. The System can be used alone or in conjunction with these digitally connected devices where the user manually controls actions for therapy decisions.

The FreeStyle Libre 2 Flash Glucose Monitoring System (herein referred to as the 'FreeStyle Libre 2 System or 'System') is an integrated continuous glucose monitoring system (iCGM) that provides continuous glucose measurements every minute to provide glucose levels, trends and alerts. The FreeStyle Libre 2 System consists of two primary components: a sensor that transmits via Bluetooth Low Energy (BLE), and a BLE enabled display device (Reader). User-initiated RFID scanning provides the user with real-time glucose measurements (glucose values) accompanied by trend information (glucose arrows) and historical glucose information (glucose graph). The user may use the sensor glucose results and information provided by the System in making treatment decisions. The System also provides configurable alerts and alarms designed to warn the user of Low Glucose, High Glucose or Signal Loss.

Here's a breakdown of the acceptance criteria and the studies conducted for the FreeStyle Libre 2 Flash Glucose Monitoring System, based on the provided document:

Acceptance Criteria and Device Performance

Study Information

1. Sample Size and Data Provenance:

   • Clinical Accuracy Studies (Study 1 & Study 2):
     • Study 1 (Adults): 146 subjects (91.1% Type 1, 8.9% Type 2 diabetes, all requiring insulin).
     • Study 2 (Pediatric): 139 subjects (98.6% Type 1, 1.4% Type 2 diabetes, all requiring insulin).
     • Age Groups in Study 2 (for YSI comparison): 77 pediatric subjects for 180 mg/dL range. Specific breakdown for 4-5 and 6-17 years where YSI was used.
     • Age Groups in Study 2 (for SMBG comparison): 8 pediatric subjects (4-5 years old) only had SMBG as comparator.
     • Data Provenance: Prospective, multi-center studies conducted at five (adult) and four (pediatric) centers in the United States.
   • Ascorbic Acid Interference Study: 60 subjects (adults, age 18 and older) with diabetes, across 4 sites.
   • Precision Study (from Clinical Studies):
     • Adults (18+): 146 subjects, 26,791 paired readings.
     • Pediatric 4-5 years: 7 subjects, 248 paired readings.
     • Pediatric 6-17 years: 130 subjects, 10,623 paired readings.

2. Number of Experts and Qualifications for Ground Truth:

   • The document implies that the ground truth for glucose values was established using a "laboratory glucose reference" (YSI) analyzer. While YSI analyzers are highly accurate, the document does not explicitly state the number of experts or their qualifications for establishing the ground truth. It refers to the YSI as the comparator method, which is a laboratory instrument, not human experts. For pediatric subjects aged 4-5 years, Self-Monitoring Blood Glucose (SMBG) tests were used as the comparator method.

3. Adjudication Method for the Test Set:

   • Not applicable in the traditional sense of medical image adjudication. The "ground truth" was established by a laboratory reference method (YSI for most cases, SMBG for some pediatric cases). There's no indication of an adjudication process involving multiple human readers to resolve discrepancies in ground truth measurements. The comparison is between the device and the reference instrument.

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

   • No, an MRMC comparative effectiveness study was not done. This type of study typically involves human readers interpreting cases with and without AI assistance. The FreeStyle Libre 2 is a standalone diagnostic device that measures glucose directly, so its performance is compared against a reference standard (YSI, SMBG), not human interpretation with or without AI.

5. Standalone (Algorithm Only) Performance:

   • Yes, the studies primarily assessed standalone performance. The device automatically provides continuous glucose measurements. The "iCGM glucose values" are the output of the algorithm. The intent is for the device to replace blood glucose testing for treatment decisions, which is a standalone use case. Human interpretation of trends and patterns is part of the overall management, but the device's accuracy itself is assessed in a standalone manner against the reference.

6. Type of Ground Truth Used:

   • Laboratory Reference and Self-Monitoring Blood Glucose (SMBG):
     • For adult subjects and pediatric subjects aged 6-17 years, the primary ground truth was established by YSI glucose measurements. YSI (Yellow Springs Instrument) is a common laboratory method considered a highly accurate reference for glucose.
     • For pediatric subjects aged 4-5 years, the ground truth was established by SMBG (Self-Monitoring Blood Glucose) tests.

7. Sample Size for the Training Set:

   • The document mentions "clinical evaluation" and "pivotal clinical studies" but does not explicitly state the sample size of a training set specifically for algorithm development or machine learning. The provided clinical studies (Study 1 and Study 2) describe the test sets used for performance evaluation, not necessarily for training the algorithm. CGM devices typically use complex calibration algorithms developed with extensive internal data that is not always detailed in 510(k) summaries.

8. How the Ground Truth for the Training Set Was Established:

   • As the document does not provide details on a specific training set or its size, it also does not elaborate on how the ground truth for such a set was established. We can infer that similar high-accuracy laboratory reference methods (like YSI) would have been used during the development and calibration phases of the device, prior to the validation studies described in the submission.

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