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It is a motor driven, indoor and outdoor transportation vehicle with the intended use to provide mobility to a disabled or elderly person limited to a seated position.

RS100 is a Mobility Scooter which provides mobility to a disabled or elderly person limited to a seated position. The Mobility Scooter is classified in the Class B and the maximum loading weight is 125kg. The scooter is a battery powered three-wheeled vehicle.

It consists Lithium-ion battery with an off-board battery charger, frame, controllers, motor, seat, back support, arm supports, control panel (including speed knob, battery gauge, power key switch, horn button, throttle control lever, charger port) two rear wheels, one front wheel, foot support.

For convenience of transportation and reduction of possible damage, the battery and arm supports can be dismantled and separately packaged. Users can also easily assemble these parts without use of the tools.

The provided document is an FDA 510(k) clearance letter for a "Magnesium alloy scooter (RS100)". This type of device, a motorized three-wheeled vehicle, is a physical mobility aid, not an AI/Software as a Medical Device (SaMD). Therefore, the concepts of acceptance criteria in the context of AI performance metrics (like sensitivity, specificity, AUC), ground truth establishment by experts, adjudication methods, MRMC studies, or training/test sets for AI algorithms simply do not apply to this device.

The "acceptance criteria" for this device are based on its compliance with international performance and safety standards, primarily the ISO 7176 series and ISO 10993-1 for biocompatibility, and IEC 60601 for electrical safety and electromagnetic compatibility. The "study that proves the device meets the acceptance criteria" refers to the non-clinical bench testing conducted to demonstrate compliance with these standards and substantial equivalence to a predicate device.

Given this, I cannot extract the information required by your prompt regarding AI/SaMD performance. I will explain why each point in your prompt is inapplicable to this document:

Inapplicability of Prompt Points:

1. A table of acceptance criteria and the reported device performance: While the document does compare the subject device's specifications to the predicate, and states that test results meet design specifications and ISO standards, it does not present these as a "table of acceptance criteria and reported device performance" in the way one would for AI metrics. The acceptance criteria are "compliance with ISO standards" and "meets design specifications," and the performance is implicitly satisfactory if it achieves this compliance.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): This would refer to a dataset used to evaluate an AI model. For a physical medical device like a scooter, the "test set" would be the physical prototypes tested. There's no "data provenance" in the sense of patient data. Testing is done on the device itself, likely within a lab setting.

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): Ground truth establishment by experts is relevant for diagnostic AI. For a scooter, the "ground truth" is whether it performs according to engineering specifications and safety standards, which is determined by objective physical measurements and adherence to specified test protocols (e.g., in the ISO standards), not expert interpretation of data.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Adjudication is for resolving disagreements among human experts labeling data for AI. Not applicable here.

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: MRMC studies evaluate the impact of AI on human reader performance, typically in imaging diagnostics. This is entirely irrelevant for a physical mobility scooter.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Standalone performance refers to an AI algorithm operating without human intervention. Not applicable to a physical device.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): As explained in point 3, the "ground truth" for a physical device's performance is adherence to defined engineering and safety standards, measured objectively.

8. The sample size for the training set: Training sets are for machine learning algorithms. This device is not an AI.

9. How the ground truth for the training set was established: As explained in point 8, not applicable.

In conclusion, this FDA 510(k) clearance document pertains to a physical mechanical device, a scooter, and not to an AI/SaMD. Therefore, addressing the specific points of your prompt as if it were an AI device is not possible with the provided information.

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