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The Kuschall Champion, Compact and Ultra-light are mechanical wheelchairs intended to provide mobility to persons restricted to a sitting position.

The Kuschall Champion, Compact and Ultra-Light are manually operated, userpropelled mechanical wheelchairs. Their intended use is to provide mobility to persons restricted to a seated position.

Each wheelchair consists primarily of a metal frame that is constructed of round and oval aluminum tubing that is welded, large rear wheels with hand rims for propelling the chair and smaller front pivoting casters for steering and turning. The Kuschall Champion is also available with a carbon fiber frame. The products are designed to be lightweight, everyday wheelchairs for both indoor and outdoor use. They are all folding, non-rigid wheelchairs.

This is a 510(k) premarket notification for a medical device (wheelchairs), not a study demonstrating device performance against acceptance criteria in the manner typically seen for AI/ML-based diagnostic devices. The document focuses on demonstrating substantial equivalence to predicate devices by fulfilling relevant performance standards.

Therefore, many of the requested fields (e.g., sample size for test set, number of experts, adjudication methods, MRMC study, standalone performance, training set details) are not applicable to this type of regulatory submission.

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Description of Device: The Kuschall Champion, Compact, and Ultra-Light are manually operated, user-propelled mechanical wheelchairs. Their intended use is to provide mobility to persons restricted to a seated position.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not explicitly stated. For mechanical devices undergoing performance standard testing, the "sample size" is typically the number of units tested to demonstrate compliance with each standard. This document implies individual wheelchairs were tested against specific engineering and safety standards, but the exact number of units is not provided.
• Data Provenance: The tests were conducted according to established international and national standards (e.g., EN, ISO, CA Bulletin). The testing was done internally by the manufacturer (Kuschall Design AG / Invacare Corporation) during the development of the wheelchairs. This can be considered prospective testing for regulatory submission purposes, but not "data provenance" in the clinical sense of patient data.

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

• Not Applicable. For mechanical device performance testing against established standards, "ground truth" as a concept typically used in AI/ML or clinical studies (e.g., expert consensus on a diagnosis) is not relevant. Compliance is assessed by engineers and technicians performing the specified tests according to the standard's methodology.

4. Adjudication method for the test set:

• Not Applicable. Adjudication methods (like 2+1, 3+1) are used for resolving discrepancies in expert interpretations of clinical data. This is not relevant for demonstrating compliance with mechanical performance standards.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done:

• No. This type of study is for evaluating the impact of an AI system on human reader performance, which is not applicable to a manual wheelchair.

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

• Not Applicable. These devices are manual wheelchairs and do not involve algorithms or AI.

7. The type of ground truth used:

• The "ground truth" in this context is the defined requirements and methodologies within each cited performance standard (e.g., EN 12183, ISO 7176-8). The device's performance is measured against these established engineering and safety specifications, rather than against clinical outcomes or expert consensus on a diagnostic finding.

8. The sample size for the training set:

• Not Applicable. The device does not involve machine learning or AI, so there is no "training set."

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

• Not Applicable. As there is no training set, this question is not relevant.

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