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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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