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"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.

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The Mobility Scooter (Model: JT10) 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 Mobility Scooter, Model JT10 is intended to be used by individuals that are able to walk but suffer from mobility limitations. It has a base with steel frame, two front wheels, two rear wheels, two anti-tip wheels, a seat, a tiller console, electric motor, electromagnetic brake, 2 rechargeable Lead-acid batteries with an off-board charger. The movement of the scooter is controlled by the rider who operates the throttle lever, speed knob and handle on the tiller console. The device is installed with an electromagnetic brake that will engage automatically when the scooter is not in use and the brake cannot be used manually. The Scooter only can be operated on the flat road.

The provided FDA 510(k) clearance letter for the Mobility Scooter (JT10) does not contain information related to an AI/ML-driven device or diagnostic study. Instead, it discusses the substantial equivalence of a physical medical device (mobility scooter) to a predicate device, based on engineering aspects and performance standards.

Therefore, I cannot extract the requested information regarding acceptance criteria, study data, sample sizes, expert involvement, ground truth, or MRMC studies, as these concepts are specifically applicable to the evaluation of AI/ML or diagnostic devices. The document focuses on physical and functional comparisons and adherence to relevant ISO standards for wheelchairs and mobility aids.

Essentially, the device cleared here is a mobility scooter, not a software or AI-based diagnostic tool. As such, the performance evaluation relies on physical testing against established ISO standards for mobility devices, not on data analysis of images or other medical data.

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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.

RS100 is a Mobility Scooter which provides mobility to a disabled or elderly person limited to a seated position. The Mobility Scooter is classified in the Class B and the maximum loading weight is 125kg. The scooter is a battery powered three-wheeled vehicle.

It consists Lithium-ion battery with an off-board battery charger, frame, controllers, motor, seat, back support, arm supports, control panel (including speed knob, battery gauge, power key switch, horn button, throttle control lever, charger port) two rear wheels, one front wheel, foot support.

For convenience of transportation and reduction of possible damage, the battery and arm supports can be dismantled and separately packaged. Users can also easily assemble these parts without use of the tools.

The provided document is an FDA 510(k) clearance letter for a "Magnesium alloy scooter (RS100)". This type of device, a motorized three-wheeled vehicle, is a physical mobility aid, not an AI/Software as a Medical Device (SaMD). Therefore, the concepts of acceptance criteria in the context of AI performance metrics (like sensitivity, specificity, AUC), ground truth establishment by experts, adjudication methods, MRMC studies, or training/test sets for AI algorithms simply do not apply to this device.

The "acceptance criteria" for this device are based on its compliance with international performance and safety standards, primarily the ISO 7176 series and ISO 10993-1 for biocompatibility, and IEC 60601 for electrical safety and electromagnetic compatibility. The "study that proves the device meets the acceptance criteria" refers to the non-clinical bench testing conducted to demonstrate compliance with these standards and substantial equivalence to a predicate device.

Given this, I cannot extract the information required by your prompt regarding AI/SaMD performance. I will explain why each point in your prompt is inapplicable to this document:

Inapplicability of Prompt Points:

1. A table of acceptance criteria and the reported device performance: While the document does compare the subject device's specifications to the predicate, and states that test results meet design specifications and ISO standards, it does not present these as a "table of acceptance criteria and reported device performance" in the way one would for AI metrics. The acceptance criteria are "compliance with ISO standards" and "meets design specifications," and the performance is implicitly satisfactory if it achieves this compliance.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): This would refer to a dataset used to evaluate an AI model. For a physical medical device like a scooter, the "test set" would be the physical prototypes tested. There's no "data provenance" in the sense of patient data. Testing is done on the device itself, likely within a lab setting.

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): Ground truth establishment by experts is relevant for diagnostic AI. For a scooter, the "ground truth" is whether it performs according to engineering specifications and safety standards, which is determined by objective physical measurements and adherence to specified test protocols (e.g., in the ISO standards), not expert interpretation of data.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Adjudication is for resolving disagreements among human experts labeling data for AI. Not applicable 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: MRMC studies evaluate the impact of AI on human reader performance, typically in imaging diagnostics. This is entirely irrelevant for a physical mobility scooter.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Standalone performance refers to an AI algorithm operating without human intervention. Not applicable to a physical device.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): As explained in point 3, the "ground truth" for a physical device's performance is adherence to defined engineering and safety standards, measured objectively.

8. The sample size for the training set: Training sets are for machine learning algorithms. This device is not an AI.

9. How the ground truth for the training set was established: As explained in point 8, not applicable.

In conclusion, this FDA 510(k) clearance document pertains to a physical mechanical device, a scooter, and not to an AI/SaMD. Therefore, addressing the specific points of your prompt as if it were an AI device is not possible with the provided information.

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The intended / indications for use for the FreeStyle Libre 3 system are as follows:

• The FreeStyle Libre 3 Flash Glucose Monitoring System is a continuous glucose monitoring (CGM) device with real-time alarms capability indicated for the management of diabetes in persons aged 4 years and older.
• It is intended to replace blood glucose testing for diabetes treatment decisions unless otherwise indicated.
• The System performs a scan every minute to measure interstitial fluid glucose concentrations.

Google Wifi point and AC2200 router with Ethernet port and USB-C port

This document is an FDA 510(k) Clearance Letter for a Mobility Scooter, not an AI/Software as a Medical Device (SaMD). Therefore, the acceptance criteria and study proving device performance are based on physical and mechanical performance standards, safety, and substantial equivalence to a predicate device, not on artificial intelligence algorithms or human-in-the-loop performance.

The request asks for information typically associated with AI/SaMD submissions, such as:

• Table of acceptance criteria and reported device performance (in terms of AI metrics)
• Sample size for test set and provenance
• Number of experts for ground truth and their qualifications
• Adjudication method
• MRMC comparative effectiveness study results
• Standalone (algorithm only) performance
• Type of ground truth used
• Sample size and ground truth for training set

None of this information related to AI/SaMD studies is applicable or present in the provided 510(k) clearance letter for a mobility scooter.

Instead, the document details non-clinical testing against established international standards (ISO, IEC, EN) for wheelchairs and mobility scooters, and biocompatibility testing.

Here's a breakdown of the relevant acceptance criteria and performance proof from the provided document for the Mobility Scooter:

Acceptance Criteria and Device Performance for Mobility Scooter (K242975)

The acceptance criteria for this Class II Mobility Scooter are primarily based on demonstrating substantial equivalence to a legally marketed predicate device (K201196) and compliance with recognized international performance and safety standards for motorized wheelchairs and scooters. The study's proof of performance relies on non-clinical testing.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly met by demonstrating compliance with the listed ISO, IEC, and EN standards, and by showing comparable performance characteristics to the predicate device. The performance data is presented as reported values from the tests.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Test Set: The document does not specify a numerical sample size (e.g., number of scooters) for the non-clinical tests. These types of tests are typically performed on a limited number of production units, as specified by the respective ISO standards.
• Data Provenance: The device manufacturer, Wuyi Aichi Industry & Trade Co., Ltd., is located in Wuyi, Zhejiang Province, China. The non-clinical testing would have been conducted by the manufacturer or a contracted test lab(s), likely in China, to demonstrate compliance with international standards for regulatory submission. The data is retrospective in the context of the 510(k) submission, meaning the tests were completed prior to the submission.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Not applicable in the context of a mobility scooter's
  non-clinical performance testing. The "ground truth" is defined by the
  established parameters and limits within the specified international
  standards (e.g., ISO 7176 series). Performance is measured objectively
  against these defined criteria.

4. Adjudication Method for the Test Set

• Not applicable. Performance is measured objectively against
  international standards. There is no concept of "adjudication" as there
  would be for human-annotated data in an AI/SaMD context.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• No, this is not applicable. MRMC studies are used for evaluating the interaction between human readers (e.g., radiologists) and AI algorithms in diagnostic tasks. This submission is for a physical medical device (mobility scooter), not an AI/SaMD.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) Was Done

• No, this is not applicable. There is no "algorithm" in the sense of AI/SaMD for this device's regulatory pathway. The "standalone" performance is the scooter's physical and mechanical performance as measured by the non-clinical tests.

7. The Type of Ground Truth Used

• The "ground truth" is established by internationally recognized performance and safety standards (e.g., ISO 7176 series, IEC 60601-1-2, EN 12184) and biocompatibility standards (ISO 10993 series). These standards define the test methodologies, acceptance limits, and evaluation criteria for safe and effective operation of mobility devices.

8. The Sample Size for the Training Set

• Not applicable. This device is a physical medical device. There is no "training set" in the context of machine learning. The design and manufacturing processes are validated against quality system regulations (21 CFR Part 820), and performance is validated through non-clinical testing on a sample of the manufactured device.

9. How the Ground Truth for the Training Set Was Established

• Not applicable. As there is no training set for a machine learning model, there is no ground truth established in that context. The "ground truth" for the device's design and manufacturing is adherence to engineering principles, regulatory requirements, and international consensus standards.

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The Powered Mobility scooteris intended for medical purposes to provide mobility to persons restricted to a sitting position.

Powered Mobility Scooter

I am sorry, but the provided text from the FDA letter (K250027) does not contain the information requested to describe the acceptance criteria and the study that proves the device meets the acceptance criteria.

The document is a clearance letter for a "Powered Mobility Scooter," a Class II medical device. It primarily covers:

• The FDA's determination of substantial equivalence to a predicate device.
• Regulatory requirements and general controls applicable to the device (e.g., Quality System Regulation, UDI Rule, adverse event reporting, labeling).
• Standard disclaimers regarding FDA's review and the manufacturer's responsibility for compliance.
• The "Indications for Use" for the device, stating it is "intended for medical purposes to provide mobility to persons restricted to a sitting position."

The letter does not include details about:

1. A table of acceptance criteria and reported device performance.
2. Sample sizes used for a test set or data provenance of a study.
3. Number of experts used to establish ground truth or their qualifications.
4. Adjudication methods.
5. Multi-Reader Multi-Case (MRMC) comparative effectiveness studies or effect sizes.
6. Standalone algorithm performance.
7. Type of ground truth used.
8. Sample size for training sets.
9. How ground truth for training data was established.

These details are typically found in the 510(k) submission itself (the "premarket notification"), which is a much more extensive document than the FDA's clearance letter. The clearance letter only announces the outcome of the FDA's review of that submission.

Therefore, I cannot fulfill your request based on the provided input.

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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 subject device, Model MS160A scooter is mainly battery-powered and driven by a DC motor, with the speed adjusted by a user-controlled panel.

The motor is driven, and the speed is adjusted by a user-controlled panel and speed knob.

The scooter consists of two folding armrests, a seat cushion, a folding frame, two front wheels, two rear drive wheels with a motor/electromagnetic brake assembly, a rechargeable lithium-ion battery, an off-board battery charger, a control panel, and a motor controller.

The MS160A scooter is designed to provide mobility for people limited to those who walk unchanged or for the elderly.

Folding design: Pull out the seat and backrest fixing pins, push the backrest forward, the seat and backrest fold into each other, release the handlebar locking knob, push the handlebar backward, the handlebar folds down, the whole scooter is folded.

Control panel: Includes speed knob, battery gauge, power key switch, horn button, throttle control lever, headlight switch, charger port, fuse.

Wheel and frame connection method: the motor is connected to the differential, the differential is connected to the frame, the wheels are mounted on both ends of the differential, the motor rotates with the differential, thus driving the scooter.

Frame design:

1. The frame of MS160A is aluminum alloy.
2. The folding mechanism is operated in two ways:
   a. In the non-riding state, pull out the seat fixing pin, push the backrest forward, the seat and backrest can be folded.
   b. Loosen the handlebar locking knob, push the handlebar forward, the handlebar will fold down.

The front wheels of the scooter are 7 inches and the rear wheels are 10 inches.

The motor of the scooter is ST-62D24-120W; the lithium-ion battery is 25.55V 10.4AH; the charger is 24V/2A.

Max. loading cannot be over than 125kg.

The maximum distance travelled with a fully charged battery is 8.2km and the maximum forward speed is 1.8m/s.

Braking time is about 2 seconds, braking distance is ≤1.5 meters.

The provided document (K242982 510(k) Summary for the Mobility Scooter (MS160A)) is a premarket notification to the FDA for a Mobility Scooter. This type of device is a physical product and not typically associated with AI/ML software or studies involving ground truth establishment by experts, multi-reader multi-case (MRMC) comparative effectiveness studies, or standalone algorithm performance.

Therefore, this document does not contain the information requested regarding acceptance criteria and study proving device meets acceptance criteria related to AI/ML product performance. The studies mentioned primarily confirm the device's compliance with physical safety and performance standards (e.g., ISO 7176 series for wheelchairs, biocompatibility).

Here's why the requested information cannot be extracted from this document:

• Device Type: A mobility scooter is a mechanical device, not imaging or diagnostic software that would rely on AI/ML algorithms to generate results requiring expert consensus or ground truth establishment.
• Study Types: The "non-clinical testing" section details adherence to established ISO standards for physical characteristics like stability, braking, dimensions, and biocompatibility. There is explicitly "No clinical study implemented for the scooter."
• Acceptance Criteria: The "acceptance criteria" for this device are its ability to meet the performance specifications outlined in the ISO standards, and to be "substantially equivalent" to a predicate device based on physical and functional comparisons. These are not acceptance criteria for AI model performance metrics like sensitivity, specificity, or AUC, which would be relevant for devices that interpret data or images.
• Ground Truth, Experts, Sample Sizes (for AI/ML): These concepts are entirely absent because they are not applicable to the type of device being submitted.

In summary, the provided document is for a physical medical device (mobility scooter) and does not involve AI/ML components or related performance studies that would utilize the concepts of ground truth, expert review, or statistical metrics like sensitivity/specificity for algorithm output.

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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.

Not Found

I apologize, but the provided text from the FDA 510(k) letter for the Mobility Scooter (S-01; S-02) does not contain any information regarding acceptance criteria, device performance studies, or details about artificial intelligence (AI) or machine learning (ML) components.

The letter is a standard FDA clearance document confirming substantial equivalence to predicate devices and outlines general regulatory requirements for medical devices. It focuses on:

• Device Name: Mobility Scooter (S-01); Mobility Scooter (S-02)
• Regulation Number/Name: 21 CFR 890.3800, Motorized Three-Wheeled Vehicle
• Regulatory Class: Class II
• Product Code: INI
• Indications for Use: Provides mobility to a disabled or elderly person limited to a seated position, for indoor and outdoor transportation.
• Regulatory Compliance: References to general controls, quality system regulations, adverse event reporting, UDI, etc.

Therefore, I cannot extract the specific information you requested regarding acceptance criteria, study details, sample sizes, expert qualifications, or AI performance metrics. This type of information would typically be found in the 510(k) summary or the full 510(k) submission document, neither of which is provided in your input.

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

WT-T4SU is for indoor or outdoor uses, and it is Lithium-ion battery powered and configured with four solid wheels, a seat, a turning tiller, and a control panel.

WT-T4SU is driven by two PU solid rear wheels (7.28" x 1.96") and steered by two PU solid castors (7.28" x 1.96"). The user can control the turning tiller to control the castors to steer the scooter's direction. Control panel has 3 functions of battery level indication, speed lever and forward/reverse selector. The user can pull the speed lever to drive forward or drive backward. Release the speed lever completely and the electromagnetic brake in the motor will be activated automatically, and the scooter will stop.

The maximum weight capacity of WT-T4SU is 264 lbs. (120 kg). Its maximum forward speed is 4 mph (6.4 km/h), and its maximum safe climbing incline angle is 6 degrees. The cruising range is 6.61 miles (10.58 km).

The provided document is a 510(k) premarket notification for an Electrical Scooter (WT-T4SU). It focuses on demonstrating substantial equivalence to a predicate device (WT-T4QP2), rather than providing detailed acceptance criteria and performance data for an AI/ML-driven medical device. Therefore, the requested information regarding acceptance criteria, study design for AI/ML performance, sample sizes, ground truth establishment, expert qualifications, and MRMC studies is not present in this document.

The document primarily describes the physical characteristics, intended use, and non-clinical performance tests (e.g., electrical safety, stability, mechanical strength) conducted to ensure the new scooter meets established standards for motorized wheelchairs and scooters. It compares the specifications of the new device to its predicate and discusses why differences do not raise new safety or effectiveness concerns.

To answer your request, here's what can be extracted and what cannot:

What can be extracted:

• Device Name: Electrical Scooter, WT-T4SU
• Predicate Device Name & 510(k) number: Electrical Scooter, model WT-T4QP, K222729
• Indications for Use: The device is intended for medical purposes to provide mobility to persons restricted to a sitting position.
• Type of Ground Truth (for non-clinical tests): Testing against established medical device standards (e.g., ISO, ANSI AAMI ES).
• Sample Size for Test Set: Implicitly, the test set for non-clinical performance tests would be the device itself or test units, not a patient population. The document lists numerous standards for testing.
• Data Provenance: Not explicitly stated as "retrospective" or "prospective" in the context of an AI study, but the tests are likely conducted in a controlled lab environment. The manufacturer is Wu's Tech Co., Ltd. from Taiwan.

What cannot be extracted from this document:

• A table of acceptance criteria and reported device performance related to AI/ML. This document pertains to a physical medical device (an electrical scooter), not an AI/ML-driven diagnostic or therapeutic device. The "performance" discussed refers to physical attributes like speed, range, and loading capacity, and compliance with various safety and performance standards for scooters.
• Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective) for an AI/ML algorithm evaluation.
• 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) for an AI/ML algorithm evaluation.
• Adjudication method (e.g. 2+1, 3+1, none) for the test set for an AI/ML algorithm evaluation.
• 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.
• If a standalone (i.e. algorithm only without human-in-the-loop performance) was done.
• The sample size for the training set (for an AI/ML algorithm).
• How the ground truth for the training set was established (for an AI/ML algorithm).

In summary, this document is a regulatory submission for a physical medical device (an electrical scooter) and does not contain the information requested about AI/ML device acceptance criteria and study design.

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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.

Not Found

The provided document is a 510(k) clearance letter from the FDA for a Mobility Scooter. It is a regulatory document and does not contain any information regarding acceptance criteria, device performance studies, sample sizes for test or training sets, ground truth establishment, or expert qualifications.

Therefore, I cannot provide the requested information based on the given input. This document is a clearance letter, confirming that the device is substantially equivalent to legally marketed predicate devices, not a study report detailing performance metrics.

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The Model R 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.

WHILL Model R is an indoor/outdoor electric scooter that is intended as a mobility aid for elderly persons or persons with reduced mobility. Providing Bluetooth-based radio communications functionalities. Model R is available in two variants, the 3-wheel model and the 4-wheel model. The 3-wheel scooter has a base with metal alloy frame, one front wheels, two rear wheels, two anti-tip wheels, a seat, an adjustable steering column, a tiller console, 2 electric motors, 2 electromagnetic brakes, a rechargeable lithium-ion battery with an off-board charger. The 4-wheel scooter has a base with metal alloy frame, two front wheels, two rear wheels, two anti-tip wheels, a seat, an adjustable steering column, a tiller console, 2 electric motors, 2 electromagnetic brakes, a rechargeable lithium-ion battery with an off-board charger. The only difference between 3-wheel model and 4-wheel model is front drive-base component. The 3-wheel model has a single front tire, while the 4-wheel model has two front tires. Besides this difference in mechanical design, other parts of the device are identical across the two models. Customers can choose a model depending on their preferences and use case. Model R has a removable lithium-ion battery, a swivel 17-inch-wide seat with two armrests and front and rear LED lights. The battery may be charged either when installed within the device or when removed. Some components are adjustable to provide user with a comfortable riding position. The inner width and the height of the armrests are adjustable in 3 positions. The seat position from the tiller is adjustable in 4 positions. The seat height is adjustable in 2 positions and the tiller angle is also adjustable. A lap belt is equipped for posture support and for safety. As with all conventional powered scooters, to drive the Model R, the user sits in the seat and grips the tiller handle around the controller unit which is positioned in front of the user. While gripping the tiller handle, the user uses their fingers to pull the right side of the throttle control lever gently to drive the device forward. The degree at which the throttle control lever is pulled is positively proportional to the speed of the device. When the throttle control lever returns to its original position after the user releases it, the device decelerates and comes to a stop. When the user uses their fingers to pull on the left side of the lever, the device drives backwards. To move to the left, the user turns the tiller handle to the left, and, to the right, the user turns the tiller handle to the right. Model R has Bluetooth-based radio communications capabilities. The user can power on Model R with a physical key, a Bluetooth smart key fob or with a dedicated smartphone app. The user can select speed profiles, turn off and on the front LED light and sound the audible warning device by operating the controller unit. User can disassemble Model R into four components, i.e. drive base, main body, seat base assembly and battery, without the use of tools when loading into a car for transportation. The device supports a maximum weight of 147 kg (325 lbs.), including the weight of the occupant and any carried items. The 3-wheel model has a theoretical continuous driving range of 18.7 km (11.6 miles) while the 4-wheel model has a theoretical continuous driving range of 17.2 km (10.7 miles).

The provided text is a 510(k) Summary for the WHILL Model R, a motor-driven indoor and outdoor transportation vehicle. It details the device's characteristics, comparison to predicate and reference devices, and results of safety and performance testing to demonstrate substantial equivalence.

However, the provided text does not describe "acceptance criteria" or a "study that proves the device meets the acceptance criteria" in the context of an AI/ML medical device performance study.

The document discusses:

• Performance testing of the physical device (e.g., static stability, dynamic stability, braking, electrical safety, biocompatibility) against existing ISO and IEC standards for wheelchairs and motorized three-wheeled vehicles. These are not "acceptance criteria" for an AI/ML algorithm's performance.
• Comparison of technological characteristics between the subject device, a predicate device, and a reference device to demonstrate "substantial equivalence." This is a regulatory pathway for physical medical devices, not a performance study for an AI/ML algorithm.
• Software Verification and Validation Testing according to ANSI AAMI IEC 62304, which is a standard for medical device software lifecycle processes, but doesn't specify performance metrics for an AI/ML model.
• FCC Radio Frequency Testing and Wireless Co-existence Testing for the device's Bluetooth functionalities, which are regulatory compliance tests for wireless communication, not AI/ML performance.
• Usability Testing according to IEC 62366-1, which focuses on user interface and safety, not AI/ML algorithm accuracy.

Therefore, I cannot extract the requested information about AI/ML acceptance criteria or a study proving it, as the provided document does not pertain to the performance evaluation of an AI/ML medical device. It describes the regulatory submission for a physical motorized vehicle.

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