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

The Wu's Electrical Scooter, WT-M4JKx, is an indoor-use electrical scooter that is battery operated. It has a base with 4 wheels, a seat and 2 armrests. The movement of the electrical scooter is controlled by the rider who uses speed control lever to control the direction and speed of an electrical scooter. The motor power is 470W. The device uses a PG S-Drive 70A electronic controller. The device is provided with an off-board battery charger (Input: AC100-240V, Output: DC 24V, 5 Amp). The maximum weight capacity of WT-M4JKx is 400 lbs (182 kg), and its maximum forward speed is 5.9 mph (9.5 km/h).

The provided text describes the regulatory clearance for a medical device called "Wu's Electrical Scooter, WT-M4JKx". The document focuses on demonstrating substantial equivalence to a predicate device (Wu's Electrical Scooter, WT-M4Jr, K032489) through compliance with recognized standards and comparison of specifications.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the safety and performance standards the device was tested against (primarily RESNA WC-1 and ISO 7176 series), and the comparison to the predicate device's performance. The reported device performance is the outcome of these tests.

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

The document does not specify a "test set" in the context of a typical clinical study with human subjects. Instead, the device's performance is demonstrated through bench testing against recognized industry standards (RESNA, ISO) and biocompatibility testing of its components.

• Sample Size: The sample size for these engineering and materials tests is not explicitly stated, but it is typically one or a small number of physical units of the device and its components, sufficient to perform the required measurements and evaluations per the standards.
• Data Provenance: The tests were conducted according to international and national standards. The country of origin of the data is not directly mentioned beyond the manufacturer being in Taiwan. The nature of these tests is prospective in the sense that the new device was built and then rigorously tested to meet pre-defined engineering and safety benchmarks. There is no indication of retrospective data.

3. Number of Experts Used to Establish Ground Truth and Their Qualifications

This information is typically relevant for studies involving subjective human assessment (e.g., image interpretation). For this device, the "ground truth" is established by the objective measurements and adherence to the specified engineering and safety standards.

• Number of Experts: Not applicable in the sense of establishing a subjective "ground truth." The experts involved would be the test engineers and laboratory personnel from the accredited testing facilities that performed the RESNA and ISO standard tests.
• Qualifications of Experts: Not specified, but implied to be qualified and accredited by virtue of conducting tests according to international standards.

4. Adjudication Method for the Test Set

Not applicable. Adjudication methods (like 2+1, 3+1) are for resolving discrepancies in human expert opinions, which is not relevant for objective engineering and biocompatibility testing. The "adjudication" here is the pass/fail result against the criteria set by the standards.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No. An MRMC comparative effectiveness study is designed to evaluate differences in diagnostic accuracy or effectiveness among human readers, often with and without AI assistance, using a set of cases. This type of study is not relevant for a motorized scooter.

• Effect Size: Not applicable.

6. Standalone Performance (Algorithm Only without Human-in-the-Loop Performance)

Yes, in the sense that the device itself (the "algorithm only") was tested against established performance and safety standards, independent of direct interaction with a human in a clinical efficacy setting. The performance metrics listed above (e.g., dynamic stability, braking distance, cruising range, maximum speed) are intrinsic to the device's design and function and were measured directly from the physical product. There is no "human-in-the-loop performance" analysis detailed for this device in the context of its regulatory clearance.

7. Type of Ground Truth Used

The ground truth used for this device is based on:

• Established engineering and safety standards: RESNA WC-1, RESNA WC-2, ISO 7176 series. The performance of the device is measured against the requirements and test methods defined by these standards.
• Objective material properties and biological response: For biocompatibility, the ground truth is established by the results of cytotoxicity, sensitization, skin irritation, and delayed-type hypersensitivity tests performed according to ISO 10993 standards.
• Comparative equivalence to a predicate device: The regulatory submission establishes that the subject device's performance is "as safe, as effective, and performs as well as" a legally marketed predicate device (K032489 Wu's Tech WT-M4Jr). This comparison itself forms a type of ground truth for regulatory purposes.

8. Sample Size for the Training Set

Not applicable. "Training set" refers to data used to train machine learning models. This device is a physical medical device (electrical scooter), not an AI/ML-driven software product.

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

Not applicable, as there is no training set for this type of device.

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

The Wu's Powered Wheelchair, Mambo 30, is an indoor-use electric wheelchair that is battery operated. It has a base with six-wheeled with a seat and armrest. The movement of the wheelchair is controlled by the rider who uses joystick, located at the right-side armrest, to control the direction and speed of the wheelchair. The device is provided with an off-board battery charger. The maximum weight capacity of Mambo 30 is 300 lbs. (136 kg), and its maximum speed is 4 mph (6.4 km/h).

The provided text describes a 510(k) premarket notification for a powered wheelchair, the Wu's Powered Wheelchair, Mambo 30. The document focuses on demonstrating substantial equivalence to a predicate device, the Wu's Powered Wheelchair, Mambo 36, primarily through performance testing against recognized standards.

Here's an analysis of the acceptance criteria and study information, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document implicitly uses international and domestic standards as acceptance criteria for various aspects of the powered wheelchair. The reported device performance is indicated by its compliance with these standards.

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

The document describes performance testing based on ISO and ANSI/RESNA standards for powered wheelchairs. These standards typically involve testing a single device or a small representative sample of the manufactured device to ensure it meets the specified engineering and safety requirements. The document does not specify a numerical sample size beyond implying testing of "the device" (singular) or its components.

The data provenance is from non-clinical tests conducted by the manufacturer, Wu's Tech Co., Ltd., based in Taiwan, R.O.C. The data is prospective in the sense that the device was actively tested against the standards to demonstrate compliance for regulatory submission.

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

This type of submission (510(k) for a powered wheelchair) does not typically involve human expert consensus for "ground truth" in the way a diagnostic AI device would. The "ground truth" here is objective compliance with established engineering and safety standards. The experts involved would be the engineers and technicians conducting the tests in accordance with the specified ISO and ANSI/RESNA standards, as well as the regulatory reviewers at the FDA who assess the submitted test reports. Their qualifications are implicitly that they are competent in performing and interpreting these engineering tests. The document does not specify the number or specific qualifications of these individuals but relies on the adherence to recognized standards.

4. Adjudication method for the test set:

Not applicable in the context of this device and testing. Adjudication methods like 2+1 or 3+1 are used in studies involving human interpretation or subjective assessments, often for diagnostic accuracy, which is not the nature of these engineering performance tests. The "adjudication" is essentially the successful completion and documentation of tests aligned with the specified 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:

No. This is a powered wheelchair, not a diagnostic imaging device utilizing AI. Therefore, an MRMC comparative effectiveness study involving human readers and AI assistance is not relevant or reported.

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

No. This is a physical medical device. "Standalone performance" in the context of an algorithm is not applicable here. The device's performance is inherently tied to its physical interaction with users and the environment, albeit controlled by electronic systems.

7. The type of ground truth used:

The ground truth used is compliance with established international and national performance and safety standards (ISO 7176 series and ANSI/RESNA WC-2 series), and biocompatibility testing standards (ISO 10993-1). This is an objective, measurable ground truth for engineering and material safety.

8. The sample size for the training set:

Not applicable. This device does not involve a "training set" in the context of machine learning or AI. Its design and performance are based on engineering principles and compliance with fixed standards, not on learning from a dataset.

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

Not applicable. See point 8.

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

The WU'S SCOOTER WT-T3H is an indoor / outdoor electric scooter that is battery operated. It has a base with three-wheeled, a seat, two 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.

1. Acceptance Criteria and Reported Device Performance

This device (WU'S ELECTRICAL SCOOTER, WT-T3H) is a motorized three-wheeled vehicle. The acceptance criteria and reported device performance are based on comparison to a legally marketed predicate device (WU'S SCOOTER WT-T3D (K032488)) and adherence to relevant performance standards.

2. Sample Size and Data Provenance

The document does not detail specific sample sizes for a test set in the traditional sense of a clinical or laboratory study with a cohort of subjects. The evaluation is primarily based on:

• Comparison to a predicate device: This involves comparing specifications and performance characteristics.
• Compliance with established engineering standards: These standards represent predefined test methods and acceptable limits.

Therefore, the concept of a "test set" as in clinical data is not directly applicable here. The data provenance is derived from engineering and safety testing performed on the device itself and its components, likely in a controlled laboratory environment. The country of origin for the submitting company is China (Taiwan).

3. Number of Experts and Qualifications

The document does not specify the number or qualifications of "experts" used to establish ground truth for a test set. The ground truth for this type of device is established through:

• Regulatory standards: The expertise is embedded in the development of the ANSI/RESNA, CISPR, EN, and IEC standards.
• Component certifications: UL certification also relies on the expertise of their testing and certification bodies.
• Comparison to the predicate: The predicate device itself has already undergone a regulatory review based on established safety and performance criteria.

4. Adjudication Method

An adjudication method in the context of expert review for a test set is not described or applicable here. The assessment is based on objective measurements against engineering standards and direct comparison of specifications to a predicate device.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

An MRMC comparative effectiveness study is not applicable to this type of device. This device is a mobility aid, and its performance is evaluated against engineering specifications and safety standards, not through diagnostic accuracy or interpretation by human readers.

6. Standalone Performance Study

Yes, a standalone performance assessment was done in the sense that the device was subjected to performance testing against established standards (EMC Report, ANSI/RESNA WC/Vol.2-1998, CISPR 11: 1990, EN61000-3-2: 1995, IEC61000-3-3: 1995). The document states that the electronic systems (controller, batteries, recharge) passed UL certification, indicating testing of these components in isolation.

7. Type of Ground Truth Used

The ground truth used is primarily:

• Engineering Standards: Compliance with recognized national and international standards for electrically powered wheelchairs, scooters, and their chargers.
• Performance Specifications of a Predicate Device: Direct comparison of key performance characteristics (e.g., weight limit, speed, climbing angle) to an already cleared device.
• Component Certifications: Independent certification (UL) for critical electronic components.

8. Sample Size for the Training Set

A "training set" in the context of machine learning or AI is not applicable to this device. This is a physical medical device, not a software algorithm that learns from data.

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

As there is no training set for a machine learning algorithm, this question is not applicable.

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

The WU'S SCOOTER WT-M3A is an indoor / outdoor electric scooter that is battery operated. It has a base with three-wheeled, a seat, two 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.

The provided text describes a 510(k) submission for a medical device, the WU'S 4-WHEELED NEO SCOOTER, WT-M3A (rebranded as WU'S NEO SCOOTER, WT-M3A in the K082963 FDA letter). This document demonstrates the device's substantial equivalence to a predicate device (WU'S SCOOTER WT-M4A, K072337) rather than providing a detailed study proving performance against acceptance criteria in the typical sense of a clinical trial or algorithm performance study.

Therefore, the requested information elements related to clinical studies, sample sizes, expert adjudication, MRMC studies, standalone algorithm performance, and ground truth establishment for AI/algorithm devices are not applicable to this document.

The "Performance Testing" section listed in the document refers to compliance with established industry standards for electrically powered wheelchairs, scooters, and their chargers. These are engineering and safety standards, not clinical performance metrics in the way usually discussed for advanced medical devices or algorithms.

Here's an attempt to answer the questions based on the available information, noting the inapplicability of many points:

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

Based on the document, "acceptance criteria" are not explicitly defined as numerical thresholds for specific performance metrics in a clinical study. Instead, the focus is on demonstrating substantial equivalence to a predicate device by meeting common design specifications and regulatory standards. The reported "performance" is primarily a comparison of features and compliance with engineering standards.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size: Not applicable. This document describes a 510(k) submission for substantial equivalence based on design comparison and engineering test compliance, not a clinical study involving a "test set" of patients or data.
• Data Provenance: Not applicable.

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)

• Not applicable. There was no clinical "test set" requiring expert ground truth establishment for this type of device submission.

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

• Not applicable.

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. This device is a mobility scooter, not an AI-assisted diagnostic or imaging device.

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

• Not applicable. The device is a physical mobility scooter; it does not contain an algorithm in this context.

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

• Not applicable in the clinical sense. The "ground truth" for this submission is established through adherence to recognized performance standards (e.g., ANSI/RESNA, CISPR, EN, IEC for EMC) and direct comparison of specifications with a legally marketed predicate device.

8. The sample size for the training set

• Not applicable. There is no AI/algorithm training set for this device.

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

• Not applicable.

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

The WU'S SCOOTER WT-M4A 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.

The provided text is a 510(k) summary for the WU'S 4-WHEELED NEO SCOOTER, WT-M4A. It describes the device, its intended use, and its substantial equivalence to a predicate device. However, it does not describe a study that uses acceptance criteria for an AI/ML device. Instead, it outlines performance testing against established standards for mobility devices and compares the new device to a predicate device.

Therefore, I cannot directly extract the requested information regarding acceptance criteria and a study proving an AI/ML device meets them from the provided text. The questions seem to be tailored for AI/ML device submissions, which this document is not.

However, I can interpret the available information in the context of the general principles of device evaluation for substantial equivalence:

Interpretation of the provided information as an analogy to acceptance criteria for non-AI devices:

In this context, the "acceptance criteria" are not for an AI model's performance metrics (like sensitivity, specificity, etc.) but rather for the overall safety and effectiveness of the medical device (the scooter). The "study" is the performance testing and comparison to a predicate device to demonstrate substantial equivalence.

Here's how the provided information relates to the spirit of your request, even if not directly answering for an AI/ML device:

1. Table of "Acceptance Criteria" and Reported Device Performance (interpreted for a medical scooter):

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

• Sample Size: Not explicitly stated as a number of individual scooters. The "test set" here refers to the device itself (WT-M4A) being tested against the stated standards and compared to the predicate device (WT-M4). Typically, this involves testing representative units.
• Data Provenance: The testing was conducted against international and national standards (ANSI / RESNA, CISPR, EN, IEC). The manufacturer is WU's TECH CO., LTD. in China (Taiwan). The report date is August 6, 2007. This suggests the testing data originates from the manufacturer's testing or accredited labs. It is a prospective evaluation of the new device's performance against established benchmarks.

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 question is not applicable to this type of device submission. There is no "ground truth" to be established by experts in the sense of an AI/ML diagnostic task (e.g., diagnosing a condition from an image).
• Instead, the "ground truth" for a device like a scooter is defined by engineering specifications, safety standards, and performance test protocols established by regulatory bodies and industry associations (e.g., ANSI / RESNA). The "experts" would be the engineers and technicians conducting the tests according to these pre-defined standards.

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

• This question is not applicable. Adjudication methods like 2+1 or 3+1 are used in clinical trials or expert review processes, particularly for AI/ML models where there might be disagreement on ground truth labeling.
• For a medical scooter, tests are typically objective and quantitative, conducted according to established procedures. The results are compared directly to the specified limits within the 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 question is not applicable. MRMC studies are specifically designed for evaluating the impact of AI assistance on human reader performance in diagnostic tasks (e.g., how AI helps radiologists detect diseases). This document pertains to a physical mobility device, not a diagnostic AI system.

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

• This question is not applicable. There is no "algorithm" in the sense of an AI/ML model for this device. The device itself is a standalone physical product. Its performance is measured directly against physical and electrical standards.

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

• As mentioned in point 3, the "ground truth" for this device is defined by established engineering and safety standards and performance test protocols (e.g., the safety limits for EMC, the strength requirements for materials, explicit performance criteria for weight capacity, cruising range, and incline angle). These are objective, measurable criteria, not subjective interpretations or clinical diagnoses.

8. The sample size for the training set:

• This question is not applicable. There is no "training set" for an AI/ML model described in this document. This refers to the data used to train an AI algorithm.

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

• This question is not applicable for the same reason as point 8.

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

The WU'S Powered Wheelchair MAMBO 36 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 is a 510(k) premarket notification for a Powered Wheelchair, which is a Class II medical device. The information provided describes the performance testing and comparison to a predicate device, as opposed to a study on software or AI performance. Therefore, many of the requested categories (such as sample size for test/training sets, expert qualifications, adjudication methods, MRMC studies, and standalone algorithm performance) are not applicable to this type of submission.

Here's an analysis of the provided text in the context of your request:

1. Table of acceptance criteria and reported device performance:

Notes on "Acceptance Criteria" and "Reported Performance":
For a physical device like a powered wheelchair, the "acceptance criteria" are generally established by the referenced standards (e.g., ANSI/RESNA WC/Vol.2-1998 for performance, CISPR 11 for EMC). The document states that the new device passed these tests or met the requirements, which implies it met the acceptance criteria implicitly defined by those standards.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

• Sample Size: Not applicable. This submission is for a physical device and references performance testing against established standards for a single device, not a data-driven study with a test set.
• Data Provenance: Not applicable. The testing appears to be physical testing of the device itself, likely performed in Taiwan where the manufacturer is located.

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):

• Not applicable. Ground truth as typically understood in AI/software validation (e.g., expert consensus on medical images) is not relevant for the performance testing of a powered wheelchair. The "ground truth" for a wheelchair's performance is adherence to engineering and safety standards.

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

• Not applicable. No expert adjudication method is mentioned or relevant for this type of device submission.

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. This is a submission for a physical medical device, not an AI or software assistant designed to improve human reader performance.

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

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

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

• Not applicable in the typical sense. The "ground truth" for this device's performance is compliance with established international and national standards for wheelchair safety and performance (e.g., ANSI/RESNA, CISPR, EN, IEC standards). This is determined through physical testing and measurement.

8. The sample size for the training set:

• Not applicable. This is not a machine learning model, so there is no "training set."

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

• Not applicable. There is no training set mentioned.

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

The WU'S 3-WHEELED NEO SCOOTER WT-T3E is an indoor / outdoor electric scooter that is battery operated. It has a base with three-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.

The document provided describes a 510(k) premarket notification for a medical device, the WU'S 3-WHEELED NEO SCOOTER WT-T3E. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than conducting a de novo clinical study with specific acceptance criteria as you might see for novel medical devices. Therefore, much of the requested information regarding detailed performance studies, sample sizes, expert ground truth, MRMC studies, and training set information is not typically found or required in a 510(k) filing that relies on substantial equivalence.

Based on the provided information, here's a breakdown of what can be extracted:

1. Table of Acceptance Criteria and Reported Device Performance

Study that Proves Device Meets Acceptance Criteria:

The primary study approach described is a comparison to a legally marketed predicate device (WU'S 3-WHEELED NEO SCOOTER WT-T3D, K032488) to establish "substantial equivalence." The sponsor performed performance testing for EMC and fire resistance, but the overall demonstration of equivalence relies on the known safety and performance profile of the predicate.

Missing Information (Not typically provided in a 510(k) for substantial equivalence):

The following information is generally not provided or required for a 510(k) where substantial equivalence is demonstrated primarily through comparison to a predicate device, especially one involving a physical product like a scooter rather than an AI/software device.

2. Sample size used for the test set and the data provenance: Not applicable. Performance testing was conducted for EMC and fire resistance, but these are typically engineering tests on the device itself, not a "test set" 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. This concept is relevant for AI devices where diagnostic accuracy is assessed against expert consensus. For a physical device like a scooter, "ground truth" would be objective measurements of physical properties and safety standards.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable.

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. This type of study is for AI-assisted diagnostic tools.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): For the performance testing, the ground truth would be the objective measurements and adherence to the specified technical standards (e.g., EMC standards, fire resistance standards). For the comparison to the predicate, the "ground truth" is the established safety and effectiveness of the legally marketed predicate device.

8. The sample size for the training set: Not applicable. This concept is for AI/machine learning models.

9. How the ground truth for the training set was established: Not applicable.

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

The WU'S Powered Wheelchair MAMBO 2 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 510(k) submission (K052559) for the WU'S POWERED WHEELCHAIR, MAMBO 2, is a premarket notification where substantial equivalence to a predicate device is claimed. As such, the concept of "acceptance criteria" and a "study that proves the device meets the acceptance criteria" in the context of performance metrics (like accuracy, sensitivity, specificity, etc.) for AI/imaging devices is not directly applicable here.

Instead, the acceptance criteria are generally related to demonstrating that the new device is as safe and effective as a legally marketed predicate device. This is primarily achieved through comparing technical characteristics, materials, and compliance with recognized standards.

Here's a breakdown of the requested information based on the provided text, adapted to the context of a medical device submission focused on substantial equivalence:

1. Table of Acceptance Criteria and Reported Device Performance

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

This submission does not describe a "test set" in the context of clinical data for performance evaluation as would be seen for an AI/imaging device. The assessment is based on a comparison of device specifications, materials, and compliance with recognized standards, primarily through engineering testing and documentation.

• Sample Size: Not applicable in the conventional sense of a clinical test set. The "samples" are the devices themselves undergoing physical and electrical testing.
• Data Provenance: The information is derived from internal design specifications, material certifications (e.g., UL), and engineering test results (such as EMC Report, ANSI / RESNA WC/Vol.2-1998, CISPR 11, EN61000-3-2, IEC61000-3-3). The manufacturer is WU'S TECH CO., LTD. located in Hsin Chu City, China (Taiwan). The testing is implied to be prospective engineering and material testing rather than retrospective clinical data analysis.

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

Not applicable. This is not a study requiring expert consensus for a "ground truth" on clinical data. The "ground truth" here is the adherence to engineering standards and safety specifications, which is verified through standard testing procedures and certifications (e.g., UL).

4. Adjudication Method for the Test Set

Not applicable. There is no clinical test set requiring adjudication in this submission.

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

No, an MRMC comparative effectiveness study was not done. This type of study is relevant for evaluating the impact of AI on human reader performance, typically in diagnostic imaging. The Mambo 2 is a powered wheelchair, and its approval process does not involve such studies.

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

Not applicable. This is not an algorithmic device.

7. The Type of Ground Truth Used

The "ground truth" in this context is established by:

• Predicate Device Characteristics: The established safety and effectiveness of the legally marketed predicate device (WU'S POWERED WHEELCHAIR, MAMBO 3, K030707).
• Engineering Standards: Compliance with recognized international and national standards for wheelchairs (e.g., ANSI / RESNA WC/Vol.2-1998, CISPR 11, EN61000-3-2, IEC61000-3-3 for electrical and mechanical safety, strength, fatigue, etc.).
• Material Certifications: Certifications for components like batteries and chargers (e.g., UL certification).
• Control System Clearances: The control systems used are FDA-cleared.

8. The Sample Size for the Training Set

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

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

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

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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-XL 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.

The provided text describes a 510(k) premarket notification for a medical device, the WU'S 4-WHEELED NEO SCOOTER, WT-XL4. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving the device meets acceptance criteria in the way one might for a novel AI or diagnostic device.

Therefore, many of the requested categories for AI/diagnostic studies are not applicable to this submission. The "study" here is primarily a comparison between the new device and a predicate device based on design specifications and compliance with relevant standards.

Here's the breakdown based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

For this specific device type (a durable medical equipment like a scooter), "acceptance criteria" are generally related to compliance with recognized standards and demonstrating that the new device's performance characteristics are at least as safe and effective as the predicate, especially for its intended use.

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

• Sample Size: Not applicable. This is not a clinical study involving human subjects or a dataset for AI. The "test set" in this context refers to the physical device itself undergoing engineering and safety testing.
• Data Provenance: Not applicable. The "data" are primarily design specifications, component certifications (UL), and engineering test reports from the manufacturer (WU'S TECH CO., LTD. in Hsin Chu City, China (Taiwan)).

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

• Not applicable. Ground truth in the context of AI/diagnostic devices typically refers to definitive diagnoses or classifications from medical experts. For a physical device like a scooter, the "ground truth" is defined by compliance with engineering standards and the device's physical performance characteristics as determined by engineering tests.

4. Adjudication method for the test set

• Not applicable. Adjudication methods like 2+1 or 3+1 are used for resolving disagreements among multiple human readers on a diagnosis. This procedure is irrelevant to the engineering and safety testing of a scooter.

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. This device is a mobility scooter, not an AI or diagnostic tool that assists human readers in interpreting medical cases.

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

• Not applicable. This device is a physical, user-operated scooter, not an algorithm.

7. The type of ground truth used

• Engineering Standards and Specifications: The "ground truth" for this submission is established by the relevant engineering and safety standards (e.g., ANSI/RESNA, CISPR, EN, IEC) that the device must comply with, and the functional specifications demonstrated through physical testing and component certifications (e.g., UL certification for electronic components). The predicate device's proven safety and effectiveness also serve as a benchmark.

8. The sample size for the training set

• Not applicable. This device is not an AI algorithm, so there is no training set in the machine learning sense.

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

• Not applicable. As there is no AI training set, this question is irrelevant.

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

The WU'S Powered Wheelchair MAMBO F120 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.

The provided text describes the 510(k) summary for the WU'S POWERED WHEELCHAIR, MAMBO F120. This documentation focuses on demonstrating substantial equivalence to a predicate device rather than detailing specific device performance against a set of acceptance criteria for a novel medical AI/ML device. Therefore, much of the requested information regarding AI/ML study design, sample sizes, ground truth establishment, expert adjudication, and MRMC studies is not applicable to this submission.

However, I can extract the relevant performance testing information that was included to support the claim of substantial equivalence.

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

The document does not present acceptance criteria in a quantitative, pass/fail table format with specific thresholds. Instead, it refers to compliance with recognized standards. The "reported device performance" is essentially that the device "passed" 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 applicable. The performance testing refers to engineering and electrical safety standards for the device itself, not a clinical study involving a test set of data.

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)

Not applicable. This is not an AI/ML device requiring expert-established ground truth.

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

Not applicable.

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.

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

The device is a physical powered wheelchair, not an algorithm. Therefore, "standalone performance" in the context of an algorithm is not applicable. The performance tests assess the standalone physical and electrical characteristics of the wheelchair.

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

For the physical and electrical safety performance testing, the "ground truth" is defined by the technical specifications and limits set forth in the referenced international and national standards (e.g., CISPR 11, EN61000-3-2, IEC61000-3-3, ANSI / RESNA WC/Vol.2-1998, UL certification). Compliance with these standards is considered the "ground truth" for the device's acceptable performance in these areas.

8. The sample size for the training set

Not applicable. This device does not involve a training set for an AI/ML algorithm.

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

Not applicable.

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