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The microFET2™ System is a dynamometer device for performing muscle tests to quantitatively measure muscle weakness caused by injury or for sports medicine applications, as well as measure general muscle strength. The device is used to record and convey an individual's ability to resist force for a specific muscle group being tested.

The microFET2™ is an ergonomically designed hand-held, battery operated, digital muscle tester. Which is an accurate, portable Force Evaluation Testing (FET) dynamometer, designed specifically for taking objective, reliable, and quantifiable muscle testing measurements, and is used to record a person's ability to resist force for a specific muscle or muscle group being tested.

The device's ergonomic design allows for its use ambidextrously, depending on stabilization requirements, by being held in either hand using an elastic strap for convenience and comfort. The size and weight of the device allows the examiner/tester to use the same procedures and methods of muscle testing techniques without causing injury to the clinician or patient.

This document is a 510(k) premarket notification decision letter from the FDA for the Hoggan Scientific MicroFET2™ System. It focuses on demonstrating substantial equivalence to a predicate device, not on proving device performance against specific clinical acceptance criteria in the way an AI/ML medical device submission would.

Therefore, many of the requested details, such as sample sizes for test sets, data provenance, number of experts for ground truth, adjudication methods, MRMC studies, standalone performance studies, and training set details, are not applicable to this type of submission for a physical medical device like a dynamometer.

The document discusses functional testing of the device (such as electrical safety and software functionality, and biocompatibility), but this is different from the clinical performance evaluation of an AI algorithm.

Here's a breakdown of what can be extracted and what cannot:

1. Table of acceptance criteria and the reported device performance:

The document broadly states that "All necessary verification steps met pre-determined acceptance criteria to confirm safety and effectiveness" and "All data met pre-determined acceptance criteria." It also lists functional tests performed. However, it does not provide a quantitative table of acceptance criteria vs. specific measured performance for the device's primary function (muscle strength measurement). It focuses on comparative equivalence to a predicate device and safety/EMC standards.

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

• Not applicable / Not provided. This device is a physical dynamometer, not an AI/ML algorithm that processes a "test set" of data in the sense of patient images or readings. The "testing" refers to hardware and firmware verification. No clinical trial data of specific patient cohorts for performance metrics is discussed.

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

• Not applicable. Ground truth in the context of an AI/ML device would refer to disease labels or measurements on patient data. This document describes a physical measurement device where the "ground truth" would be the actual force applied, which is measured by a calibrated load cell. Expert labeling is not relevant here.

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

• Not applicable. No expert adjudication process is described as it's not a performance evaluation involving subjective human interpretation of outputs.

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

• Not applicable. This is not an AI-assisted device. It's a direct measurement tool.

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

• Not applicable. This device is a physical tool for direct measurement of force; there is no "algorithm only" performance separate from the device's operation.

7. The type of ground truth used:

• The "ground truth" for a dynamometer's accuracy would typically be established by using calibrated weights or force meters to verify the load cell's readings against known forces within its measurement range. The document states "Resistance based strain gauge (Load Cell) with microprocessor to convert analog signal to digital, calibrated data," implying that calibration is an inherent part of its operation. It does not elaborate on the specific calibration process or "ground truth" for accuracy testing.

8. The sample size for the training set:

• Not applicable. There is no "training set" in the context of an AI/ML algorithm.

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

• Not applicable. There is no "training set" for this device.

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