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The GEN 2 Mechanical Wheelchair is intended to provide mobility to persons restricted to a seated position.

The GEN 2 mechanical wheelchair is a highly adaptable all-purpose wheelchair created for use in developing countries, while allowing for a healthy, personalized fit. The wheelchair features adjustable seating, large castor wheels, an extra-thick cushion and adjustable footrests.

This is a 510(k) premarket notification for a mechanical wheelchair, not an AI/ML device. Therefore, the requested information regarding acceptance criteria and study details relevant to AI/ML device performance (like sample size for test/training sets, expert qualifications, adjudication methods, MRMC studies, or standalone algorithm performance) is not applicable or present in the provided text.

The document discusses the substantial equivalence of the GEN 2 Mechanical Wheelchair to a predicate device based on design, materials, intended use, and non-clinical performance data against international standards for mechanical wheelchairs.

Here's the relevant information that can be extracted:

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

The document doesn't present a table of "acceptance criteria" in the typical sense for a pass/fail study like an AI/ML device. Instead, it compares the technological characteristics of the GEN 2 wheelchair to a predicate device (Ki Mobility Catalyst Wheelchair) to establish substantial equivalence.

The reported device performance for the GEN 2 Mechanical Wheelchair is that it underwent non-clinical testing to international standards, as listed below. The conclusion is that "The non-clinical test results demonstrate the GEN 2 Mechanical Wheelchair does not raise any issues regarding safety and effectiveness" and "supports a determination of substantial equivalence to mechanical wheelchairs previously cleared by FDA."

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

This information is not applicable to this type of device submission. The testing performed is non-clinical, focusing on mechanical properties and compliance with safety standards, rather than evaluating performance on a test set of data with human subjects or a dataset similar to AI/ML evaluations. There's no "test set" in the context of data used for algorithm evaluation.

The provenance of the data is that it comes from non-clinical testing against specified ISO standards.

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)

Not applicable. There is no test set with ground truth established by experts as would be the case for an AI/ML diagnostic device. The evaluation is based on engineering and materials testing against international standards for wheelchair safety and performance.

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

Not applicable. No adjudication method is described because there isn't a test set with expert-established ground truth.

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/ML device, and no MRMC study was conducted.

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

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

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

The "ground truth" in this context refers to the international standards for mechanical wheelchairs (e.g., ISO 7176 series and their specified test methods, ASTM, JIS standards). The device's performance is compared against these engineering standards to ensure safety and effectiveness.

8. The sample size for the training set

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

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

Not applicable. This is not an AI/ML device that requires a training set or its associated ground truth establishment.

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