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As a motorized portable electric vehicle for indoor or outside use by persons with medical conditions requiring assistance.

A battery operated lightweight portable mobility device designed for personal travel indoors and limited outdoor use and available in 4 wheel configurations. It is used in a sitting position and operated by a hand controlled throttle located on the handle bars. Braking is automatically controlled when releasing the throttle by the electronic controller and magnetic motor brake. The product is supplied ready to use with a battery and charger and it can be disassembled and folded for transport. Steel frame, battery cycle life of Sealed Lead Acid battery is 400 charging times, 24V15ah Lithium battery is 1000 charging times, 24V12ah Lithium battery is 800 charging times.

The provided document is a 510(k) summary for the "SupaScoota" Motorized Vehicle, Spartan (Sumo) / Spartan SP (Sumo SP). It details the device's characteristics and its substantial equivalence to a predicate device, rather than a clinical study evaluating the device's performance against specific acceptance criteria for AI-related software. Therefore, much of the requested information regarding AI-specific acceptance criteria and study details cannot be extracted from this document.

However, I can provide information based on the listed non-clinical testing performed to establish safety and effectiveness for this type of medical device (a motorized vehicle).

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

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

The document does not explicitly state "acceptance criteria" in a table format with corresponding "reported device performance" for each criterion. However, it lists several ISO 7176 standards that the device underwent testing for. The general acceptance criterion implied is compliance with these standards. The document states: "All test results are in compliance with it and were similar to predicate device."

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This information is not provided in a 510(k) summary for a physical device. Non-clinical testing typically involves specific test samples of the device and its components. "Data provenance" as typically understood in AI studies (e.g., country of origin of data, retrospective/prospective) is not applicable here as these are performance tests of the device itself.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This is not applicable. The "ground truth" for the non-clinical tests of a motorized vehicle are established by the international standards (ISO) themselves through defined testing methodologies and equipment, not by human experts evaluating outputs.

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

This is not applicable, as there is no expert adjudication for the performance of a physical device against engineering standards.

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

This is not applicable. This document describes a physical motorized medical device, not an AI or software device that assists human readers.

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

This is not applicable. This document is about a physical medical device.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)

The "ground truth" for the non-clinical tests of this motorized vehicle is defined by the technical specifications and performance requirements outlined in the referenced ISO and IEC standards. For example, for "Static stability test" (ISO 7176-1), the ground truth is simply whether the device remains stable under defined static conditions.

8. The sample size for the training set

This is not applicable. This device is not an AI/ML algorithm that requires a training set.

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

This is not applicable. This device is not an AI/ML algorithm.

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