Search Results

The NISSIN Pediatric Series Wheelchair is intended to be used to empower persons physically challenged to a sitting position by providing a means of mobility.

The NISSIN Pediatric Series Wheelchair consists of typical components found on most wheelchairs, THE NOON T ourants offices , backrest, seat frame, cushion, footrest and casters. Many of these suon do patil harities, an a range of sizes, shapes, angles, forms, materials or coverings. Components and a allow the chairs to be configured to meet the specific desires and needs of the user.

This looks like a clearance document for a medical device that does not use AI. The questions you're asking are geared towards AI/ML-enabled devices, so many of the requested fields are not applicable.

However, I will extract the information that is available within the provided text.

Acceptance Criteria and Device Performance Study for NISSIN Pediatric Series Wheelchair

1. Table of Acceptance Criteria and Reported Device Performance

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 submission references compliance with guidance documents and ISO standards, which typically involve testing, but details about the specific test set sample size or data provenance are not included in this summary.

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 information is not applicable as the device is a mechanical wheelchair and not an AI/ML diagnostic tool requiring expert-established ground truth in the medical imaging sense. The "ground truth" for a mechanical device would be its adherence to engineering specifications and safety standards, which are evaluated through standardized tests rather than expert consensus on medical data.

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

This information is not applicable for a mechanical wheelchair. Adjudication methods like 2+1 or 3+1 are typically used in medical imaging studies involving multiple human readers for diagnostic agreement, 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

This information is not applicable as the device is a mechanical wheelchair and does not involve AI assistance for human readers.

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

This information is not applicable as the device is a mechanical wheelchair and does not involve an algorithm. Performance evaluation for such a device would be based on its physical characteristics and functionality.

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

The "ground truth" for this device's performance is its compliance with established engineering standards and regulatory guidance documents (e.g., ISO 7176 and the FDA's 510(k) guidance for mechanical wheelchairs). This is verified through physical testing and documentation of conformity, rather than medical "ground truth" types like pathology or expert consensus on clinical data.

8. The sample size for the training set

This information is not applicable as the device is a mechanical wheelchair and does not involve a training set as understood in AI/ML contexts.

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

This information is not applicable as the device is a mechanical wheelchair and does not involve a training set.

Ask a specific question about this device