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The intended use of the Pride Mobility Products Corporation Q6 Edge EM is to provide mobility to persons limited to a seated position that have the capability of operating a Powered Wheelchair.

The Q6 Edge EM is a Powered Wheelchair having a digital controller, electrical system, motors, encoders, batteries, seating, actuator driven stability system, and frame. The Q6 Edge EM is equipped with electronic, regenerative disc brakes, off-board battery charger, removable 12 Volt U1 batteries, and front and rear caster wheels.

The Q6 Edge EM is designed with ultimate safety, stability, and performance in mind. The Powered Wheelchair is designed for, but not limited to Pride Mobility Products Corporation, providers/retailers and their consumers.

The Q6 Edge EM will incorporate all of the same features as current devices with elevation, but will utilize a secondary power actuator assembly in the base to lock the casters for additional stability. The actuator will be controlled through the current control system. The addition of the actuator assembly is the reason for this change.

The Q6 Edge EM utilizes a digital controller with a Class 1 Bluetooth®. The actual power output of the Bluetooth® device is 10 dBm (which is 10 mW) maximum output power at 50 Ohm impedance, and the maximum range is 40 m. The specific RF wireless technology type for this Bluetooth® is a Co-existence with IEEE 802.11 (AWMA, AFH). The Bluetooth® has 128-bit encryption security. The Bluetooth® allows clients to connect to a computer, smartphone, and tablet. The Bluetooth® is not active during driving or seating operation.

The provided document is a 510(k) premarket notification for a powered wheelchair, the Q6 Edge EM. It does not contain information about studies related to AI, machine learning, or complex algorithms that would typically have acceptance criteria focusing on performance metrics like sensitivity, specificity, or AUC.

Instead, the document focuses on demonstrating substantial equivalence to a predicate device (Pride Mobility Jazzy 600, K042612) by adhering to established performance and safety standards for powered wheelchairs.

Therefore, many of the requested points regarding AI/ML device studies (such as sample size for test set/training set, expert qualifications, adjudication methods, MRMC studies, standalone performance with specific performance metrics, and detailed ground truth establishment for AI models) are not applicable to this type of medical device submission.

Here's a breakdown of the available information based on your request, with an emphasis on what is present in the document:

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

The document does not present a formal table of acceptance criteria with specific numerical performance targets and reported results as one would typically see for an AI/ML diagnostic or prognostic device. Instead, acceptance is implied by compliance with recognized standards for powered wheelchairs.

The study proving the device meets these criteria is the "Non-Clinical Testing" detailed in the document, which involved demonstrating compliance with the listed 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 provided in the document as it pertains to non-clinical testing against standards for wheelchair safety and performance, not a clinical study on a population. The testing involves specific units of the device under controlled lab conditions rather than patient 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)

This information is not applicable and therefore not provided. "Ground truth" in the context of AI/ML usually refers to labels or outcomes derived from expert review or clinical data. For this device, "ground truth" equates to mechanical and electrical engineering principles and compliance with established performance standards. The expertise would be in mechanical, electrical, and biomechanical engineering, and regulatory compliance, but not in medical image interpretation or clinical diagnosis.

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

This information is not applicable and therefore not provided. Such methods are used for resolving discrepancies in expert labeling during AI/ML model development.

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, an MRMC comparative effectiveness study was not done. This type of study is relevant for AI-assisted diagnostic or decision-support systems which is not the nature of this device.

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

This information is not applicable. The Q6 Edge EM is a powered wheelchair; it is a physical device operated by a human user, not a standalone algorithm. While it has a digital controller and Bluetooth, there is no "standalone algorithm performance" in the sense of an AI/ML model being evaluated.

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

As described in point 3, the "ground truth" for this device's non-clinical testing is based on compliance with established international and national standards (e.g., RESNA, ISO, California Technical Bulletin). This involves measurement of physical properties, electrical safety, flammability, and biocompatibility in a laboratory setting, not clinical outcomes or pathology.

8. The sample size for the training set

This information is not applicable and therefore not provided. This device is not an AI/ML algorithm that requires a training set.

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

This information is not applicable, as there is no training set for an AI/ML algorithm.

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