Search Results

"It is a motor driven, indoor and outdoor transportation vehicle with the intended use to provide mobility to a disabled or elderly person limited to a seated position."

The product is a Mobility Scooter which provides transport for the disabled and the elderly and to be used both indoors and outdoors.

This scooter is a battery powered four wheeled vehicle. It consists two Lead-acid batteries (in the battery box) with an off-board battery charger, frame, a seat, a back support, control panel (including the key switch, throttle control lever, horn button, speed adjustment dial, and the battery condition meter), two foldable handles, two rear drive wheels with motor/ e-magnetic brake components, two front wheels, anti-tip devices, an external battery charger, a control panel and a motor controller. For convenience of transportation and reduction of possible damage, the battery and the seat unit can be dismantled and separately packaged.

The front wheels of the scooter are 8 inches and the rear wheels are 9 inches.
The motor of the scooter is 24VDC,150W,4200RPM; the Lead-acid Battery is 12 V, 22 Ah, 18.7 Ah; the charger is 24V/2A.
Max. loading cannot be over than 180kg.
The maximum distance travelled with a fully charged battery is 17.5km and the maximum forward speed is 1.6m/s(5.8km/h).
Braking time is less than 2 seconds, braking distance is ≤1.0 meters.

This document is an FDA 510(k) Clearance Letter for a Mobility Scooter (MS160C). It is not an AI/ML medical device, and therefore does not contain the information typically associated with studies proving the performance of an AI/ML algorithm.

The request asks for details like "sample size used for the test set," "number of experts used to establish ground truth," "adjudication method," "MRMC comparative effectiveness study," and "standalone performance," which are specific to the evaluation of AI/ML software. This information is irrelevant to a physical device like a mobility scooter.

Therefore, I cannot provide the requested information as it is not present in the provided document. The document focuses on demonstrating substantial equivalence to a predicate device through non-clinical performance and safety testing against established standards (ISO, IEC).

Here's why each point in your request cannot be answered based on the provided text:

1. A table of acceptance criteria and the reported device performance: While the document lists performance characteristics in "Table 2 - Performance Comparison", these are not presented as "acceptance criteria" in the sense of accuracy/sensitivity/specificity targets for an AI algorithm. Instead, they are comparisons to a predicate device and compliance with established ISO standards. I can extract performance characteristics, but not "acceptance criteria" for an AI model.
2. Sample sized used for the test set and the data provenance: Not applicable. This refers to data for an AI model. The mobility scooter underwent physical product testing.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth and expert review are for AI model validation.
4. Adjudication method: Not applicable. Adjudication is for resolving discrepancies in AI model output or expert readings.
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. MRMC studies are for evaluating AI's impact on human performance in diagnostic tasks.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable. This device is a physical product, not an algorithm.
7. The type of ground truth used (expert concensus, pathology, outcomes data, etc): Not applicable. "Ground truth" is a concept for AI model training and validation.
8. The sample size for the training set: Not applicable. This refers to data for an AI model.
9. How the ground truth for the training set was established: Not applicable. This refers to data for an AI model.

However, I can extract information related to the device's performance testing based on the provided document:

Study that proves the device meets requirements (Non-Clinical Testing):

The study was a series of non-clinical tests conducted to verify that the proposed device (Mobility Scooter MS160C) met all design specifications and was substantially equivalent (SE) to the predicate device (Mobility Scooter Model: W3468, K220207).

1. Table of Acceptance Criteria (as reported performance against standards, not AI-specific criteria) and the Reported Device Performance:

The document describes compliance with various ISO and IEC standards as the basis for demonstrating safety and effectiveness. The "acceptance criteria" are implied by adherence to these standards, and the "reported performance" is demonstrated by the test results confirming this compliance.

Key Performance Characteristics (from Table 2 - Performance Comparison):

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

• Sample Size: The document does not specify the number of units tested. For physical device performance testing against standards, it's common to test a representative number of units, but this specific number is not provided in the summary.
• Data Provenance: The tests are non-clinical, meaning they were conducted in a laboratory or controlled setting, presumably as part of the manufacturer's (Suzhou Master Machinery Manufacturing Co., Ltd., China) quality assurance and regulatory submission process. There is no mention of data from human subjects or specific geographical origin for "data provenance" in the 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 as this is a physical device, not an AI/ML algorithm requiring expert ground truth for diagnostic or similar tasks. The "ground truth" for the device's performance is established by objective measurements against international standards.

4. Adjudication method:

• Not applicable for a physical medical device.

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 type of study is specific to AI/ML devices interacting with human interpreters.

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

• Not applicable. This is a physical mobility scooter, not an algorithm.

7. The type of ground truth used:

• The "ground truth" for the mobility scooter's performance is established by objective measurements and conformance to international performance, safety, and quality standards (e.g., ISO 7176 series, IEC 60601-1-2, ISO 10993 series). This is not an "expert consensus" or "pathology" in the AI/ML context.

8. The sample size for the training set:

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

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

• Not applicable.

Ask a specific question about this device