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The intended / indications for use for the FreeStyle Libre 3 system are as follows:

• The FreeStyle Libre 3 Flash Glucose Monitoring System is a continuous glucose monitoring (CGM) device with real-time alarms capability indicated for the management of diabetes in persons aged 4 years and older.
• It is intended to replace blood glucose testing for diabetes treatment decisions unless otherwise indicated.
• The System performs a scan every minute to measure interstitial fluid glucose concentrations.

Google Wifi point and AC2200 router with Ethernet port and USB-C port

This document is an FDA 510(k) Clearance Letter for a Mobility Scooter, not an AI/Software as a Medical Device (SaMD). Therefore, the acceptance criteria and study proving device performance are based on physical and mechanical performance standards, safety, and substantial equivalence to a predicate device, not on artificial intelligence algorithms or human-in-the-loop performance.

The request asks for information typically associated with AI/SaMD submissions, such as:

• Table of acceptance criteria and reported device performance (in terms of AI metrics)
• Sample size for test set and provenance
• Number of experts for ground truth and their qualifications
• Adjudication method
• MRMC comparative effectiveness study results
• Standalone (algorithm only) performance
• Type of ground truth used
• Sample size and ground truth for training set

None of this information related to AI/SaMD studies is applicable or present in the provided 510(k) clearance letter for a mobility scooter.

Instead, the document details non-clinical testing against established international standards (ISO, IEC, EN) for wheelchairs and mobility scooters, and biocompatibility testing.

Here's a breakdown of the relevant acceptance criteria and performance proof from the provided document for the Mobility Scooter:

Acceptance Criteria and Device Performance for Mobility Scooter (K242975)

The acceptance criteria for this Class II Mobility Scooter are primarily based on demonstrating substantial equivalence to a legally marketed predicate device (K201196) and compliance with recognized international performance and safety standards for motorized wheelchairs and scooters. The study's proof of performance relies on non-clinical testing.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly met by demonstrating compliance with the listed ISO, IEC, and EN standards, and by showing comparable performance characteristics to the predicate device. The performance data is presented as reported values from the tests.

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

• Sample Size for Test Set: The document does not specify a numerical sample size (e.g., number of scooters) for the non-clinical tests. These types of tests are typically performed on a limited number of production units, as specified by the respective ISO standards.
• Data Provenance: The device manufacturer, Wuyi Aichi Industry & Trade Co., Ltd., is located in Wuyi, Zhejiang Province, China. The non-clinical testing would have been conducted by the manufacturer or a contracted test lab(s), likely in China, to demonstrate compliance with international standards for regulatory submission. The data is retrospective in the context of the 510(k) submission, meaning the tests were completed prior to the submission.

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

• Not applicable in the context of a mobility scooter's
  non-clinical performance testing. The "ground truth" is defined by the
  established parameters and limits within the specified international
  standards (e.g., ISO 7176 series). Performance is measured objectively
  against these defined criteria.

4. Adjudication Method for the Test Set

• Not applicable. Performance is measured objectively against
  international standards. There is no concept of "adjudication" as there
  would be for human-annotated data in an AI/SaMD context.

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

• No, this is not applicable. MRMC studies are used for evaluating the interaction between human readers (e.g., radiologists) and AI algorithms in diagnostic tasks. This submission is for a physical medical device (mobility scooter), not an AI/SaMD.

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

• No, this is not applicable. There is no "algorithm" in the sense of AI/SaMD for this device's regulatory pathway. The "standalone" performance is the scooter's physical and mechanical performance as measured by the non-clinical tests.

7. The Type of Ground Truth Used

• The "ground truth" is established by internationally recognized performance and safety standards (e.g., ISO 7176 series, IEC 60601-1-2, EN 12184) and biocompatibility standards (ISO 10993 series). These standards define the test methodologies, acceptance limits, and evaluation criteria for safe and effective operation of mobility devices.

8. The Sample Size for the Training Set

• Not applicable. This device is a physical medical device. There is no "training set" in the context of machine learning. The design and manufacturing processes are validated against quality system regulations (21 CFR Part 820), and performance is validated through non-clinical testing on a sample of the manufactured device.

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

• Not applicable. As there is no training set for a machine learning model, there is no ground truth established in that context. The "ground truth" for the device's design and manufacturing is adherence to engineering principles, regulatory requirements, and international consensus standards.

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