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The device is intended for medical purposes to provide mobility to persons restricted to a seated position.

LERADO, Powered Wheelchair, DF4110 series include the devices with the same mechanic and electric controlling structure but with different colors of body and veneer. The LERADO Powered Wheelchair, DF4110 is an indoor / outdoor wheelchair that has a base with four-wheeled with a seat and is battery operated. The device can be disassembled for transportation, easily foldable and is provided with an off-board battery charger. The movement of the wheelchair is controlled by the rider who uses hand controls located at the top of the steering column.

The provided text describes a 510(k) premarket notification for the LERADO Power Wheelchair, Model DF4110. This document focuses on demonstrating substantial equivalence to a predicate device, rather than presenting a study proving that the device meets specific acceptance criteria in the context of clinical performance or diagnostic accuracy. Instead, the submission describes adherence to various ISO and ANSI/RESNA standards for powered wheelchairs, materials testing, and a comparison of specifications with a predicate device.

Therefore, many of the requested items (e.g., sample sizes for test/training sets, data provenance, number of experts for ground truth, adjudication methods, MRMC studies, standalone performance, type of ground truth for training) are not applicable or cannot be extracted directly from this type of regulatory submission for a powered wheelchair.

Here's an attempt to answer the questions based on the available information:

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

The acceptance criteria are implicitly defined by the standards the device claims to meet and by demonstrating performance comparable to the predicate device. The "reported device performance" are the specifications and compliance with these standards.

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 the document. The document describes compliance with international standards (ISO, ANSI/RESNA) which typically involve testing samples of the device, but the specific sample sizes used by the manufacturer for their internal testing or for the certification to these standards are not detailed. The manufacturer is LERADO Global (MACAO Commercial Offshore) Limited, based in Macau, China, suggesting the testing likely occurred in connection with their manufacturing operations, possibly with test labs in China or elsewhere. The nature of these tests (e.g., durability, stability) is generally prospective as they are performed on 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 in the context of this device submission. This document relates to a physical medical device (a powered wheelchair) and its performance against engineering standards, not to an AI/diagnostic algorithm where expert-established ground truth for a test set would be relevant.

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

This is not applicable as it pertains to human interpretation/adjudication of data, which is not relevant for the performance testing of a powered wheelchair 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 in the context of this device. MRMC studies are used for diagnostic or AI-assisted interpretation, not for mechanical devices like wheelchairs.

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

This is not applicable as there is no "algorithm only" component described for this powered wheelchair.

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

The "ground truth" in this context refers to the established engineering standards and specifications (e.g., ISO 7176 series, ANSI/RESNA WC Vol.2, EN 1021, ANSI/BIFMA X5.1). The device's performance is measured against these objective, quantifiable standards.

8. The sample size for the training set

This is not applicable. Powered wheelchairs are physical devices that do not typically use "training sets" in the sense of machine learning algorithms. Their design and manufacturing processes might involve iterative testing and refinement, but this is distinct from an AI training set.

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

This is not applicable for the same reasons as #8.

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The device is intended for medical purposes to provide mobility to persons restricted to a seated position.

The LERADO Power Wheelchair, PB is an indoor / outdoor Powered Wheelchair that is battery operated. It has a base with four-wheeled with a seat. The movement of the Wheelchair is controlled by the rider who uses hand controls located at the top of the steering column. The device can be disassembled for transport and is provided with an onboard battery charger.

The provided text is a 510(k) summary for the LERADO Power Wheelchair, PB. It does not describe an AI/ML powered medical device, but rather a traditional powered wheelchair seeking substantial equivalence to a predicate device. Therefore, much of the requested information regarding acceptance criteria for an AI/ML device and specific study details (like sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC studies, standalone performance, and ground truth establishment) is not applicable or present in this document.

However, I can extract information related to the device's performance testing and comparison to the predicate device to infer some "acceptance criteria" through the lens of substantial equivalence.

Here's the closest interpretation of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not applicable or not specified in the document. This is not an AI/ML device where a 'test set' of data would be used to evaluate an algorithm's performance. The "test set" here would refer to the physical device undergoing performance testing.
• Data Provenance: The device underwent performance testing according to international standards (listed above). The document doesn't specify if these tests involved human subjects or specific data sets in the way an AI/ML study would. It refers to the testing of the physical wheelchair.

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

• Not applicable. This is a traditional device. The "ground truth" is established by the specifications and performance standards themselves, not by expert consensus on data interpretation.

4. Adjudication method for the test set

• Not applicable for the type of device and submission. Adjudication methods like 2+1 or 3+1 are typically used for expert review of medical images or other complex data where varying interpretations are possible to establish a consensus ground truth, which is not relevant 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

• No. This is not an AI/ML device, and no MRMC study was performed or is relevant for this type of submission.

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

• No. This is not an AI/ML device.

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

• The "ground truth" for this device's performance is established by engineering and safety standards (e.g., ANSI / RESNA WC/Vol.2-1998, CISPR 11, EN61000-3-2, IEC61000-3-3, UL certification) which the device is tested against. The claim of substantial equivalence is based on meeting these standards and having similar performance characteristics to a legally marketed predicate device.

8. The sample size for the training set

• Not applicable. This is not an AI/ML device, 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 for an AI/ML model, there is no ground truth to be established for such a set.

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