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

The WU'S 4-WHEELED SCOOTER WT-T4D is an indoor / outdoor electric scooter that is battery operated. It has a base with four-wheeled with a seat, armrests, and a front basket. The movement of the scooter 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.

This document describes a 510(k) premarket notification for a medical device, the WU'S 4-Wheeled Scooter, WT-T4D.

Here's an analysis of the provided text in relation to your request about acceptance criteria and a study proving the device meets them:

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: The document does not specify a separate "test set" in the context of typical AI/machine learning studies. The "testing" referred to is performance testing for EMC compliance and a comparison study to a predicate device. For the EMC compliance, it's implied that the device itself underwent these tests. For the comparison, the direct "sample size" isn't a relevant metric in the way it is for clinical trials or AI validation.
• Data Provenance: The document indicates that the testing was performed, but does not explicitly state the country of origin for the performance test data. The submitter is from China (Taiwan). The comparison is to a predicate device, which would largely be based on the specifications and certifications of that device. The clinical "data" in this context is the comparison to existing standards and a legally marketed predicate device.

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

• Experts: This type of medical device submission (510(k) for a scooter) does not involve expert consensus in the way a diagnostic AI might. The "ground truth" here is established by engineering standards and regulatory compliance (UL certification for electronics, and specific performance standards like ANSI/RESNA WC/Vol.2-1998 for physical performance). There are no enumerated "experts" establishing a ground truth in the sense of reviewing medical images or patient records. The FDA's review process itself involves experts in device regulation and safety.

4. Adjudication method for the test set:

• Adjudication Method: Not applicable in the context of this device and submission. Adjudication methods like "2+1" or "3+1" are typically used in clinical trials or AI validation studies where multiple human readers interpret data, and discrepancies need to be resolved. This submission relies on direct compliance with engineering standards and comparison to a predicate device.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance:

• MRMC Study: No, an MRMC comparative effectiveness study was not done. This device is a mobility scooter, not a diagnostic or prognostic tool that would typically involve human readers interpreting data. Therefore, the concept of "human readers improve with AI vs without AI assistance" is not applicable here.

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

• Standalone Performance: Not applicable. This device is a physical product (a scooter) with electronic components. Its "performance" is its functionality, safety, and compliance with standards, not an algorithm's output. While it has an electronic controller, the "standalone performance" isn't measured in the same way as a solely algorithmic device. The performance tests (EMC, speed, climbing angle, etc.) are inherent to the device itself.

7. The type of ground truth used:

• Ground Truth: The "ground truth" for this device's safety and effectiveness is established by:
  • Regulatory Standards: Compliance with specific industry standards (e.g., ANSI / RESNA WC/Vol.2-1998, CISPR 11, EN61000-4-2, IEC61000-4-3 for EMC, and UL certification for electronics).
  • Predicate Device Equivalence: The specifications and proven safety/effectiveness of the legally marketed predicate device (WU'S 4-WHEELED NEO SCOOTER WT-T3D). The new device is deemed "substantially equivalent" based on matching key safety and performance characteristics with the predicate device.

8. The sample size for the training set:

• Training Set Sample Size: Not applicable. This is not an AI/machine learning device that requires a "training set" for an algorithm. The device's design is based on engineering principles and the existing predicate device.

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

• Training Set Ground Truth Establishment: Not applicable, as there is no "training set" for this type of device.

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