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EMS is used for:

• Relaxation of muscle spasms
• Prevention or retardation of disuse atrophy
• Increasing local blood circulation
• Muscle re-education
• Maintaining or increasing range of motion
• Immediate postsurgical stimulation of calf muscles to prevent venous thrombosis

TENS is used for:

• Symptomatic relief and management of chronic, intractable pain
• Post-surgical acute pain
• Post-trauma acute pain

EVE Synergy has Synergy handpiece (1EA), Pro handpiece (2EA), Micro cable, Tip (Ball type, T type, COMB type) and Power adaptor & cable.

EVE Synergy has Synergy mode and Pro mode.

Synergy mode has radio frequency and low frequency functions using a synergy handpiece. The Synergy handpiece has a temperature sensor, so the output is automatically turned off when the skin surface temperature exceeds 43℃.

Pro mode has low frequency function that uses the Pro handpiece with tip.

It should be noted that the device has radio frequency (RF Mode) indicated for relief of minor muscle aches and pain, relief of muscle spasm, temporary improvement of local blood circulation. This RF mode was previously reviewed by the FDA under K212253 and was cleared under regulation 21 CFR 878.4400 (Product code PBX). In the subject submission, there are no changes being proposed related to the RF Mode, and, hence, the review of the RF Mode is not within the scope of the subject 510(k) submission.

The provided text is a 510(k) clearance letter and an accompanying 510(k) summary for the EVE Synergy (EVE-20M) device. This document focuses on demonstrating substantial equivalence to previously cleared predicate devices, rather than proving the device meets specific acceptance criteria through a comparative effectiveness study involving AI assistance or human-in-the-loop performance.

Therefore, many of the requested details regarding acceptance criteria for AI performance, specific study designs (MRMC, standalone), ground truth establishment for AI training/test sets, and sample sizes for these, are not present in this type of regulatory submission. This document primarily describes the physical and electrical characteristics of the device and compares them to predicate devices, along with standard electrical safety, EMC, and software validation testing.

Here's a breakdown of the requested information based on the provided text, highlighting what is present and what is not:

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

The document does not present "acceptance criteria" in the traditional sense of a specific performance metric (like accuracy, sensitivity, specificity) for a clinical outcome or an AI model's performance. Instead, it demonstrates compliance with safety and performance standards and similarity to predicate devices.

The "acceptance criteria" are implicitly met by showing:

• Compliance with various IEC standards (IEC 60601-1, IEC 60601-1-2, IEC 60601-2-10, IEC 62304) for electrical safety, EMC, and software.
• The device's technical specifications (e.g., output voltage, current, pulse width, frequency, power density) are comparable to or within the safe limits established by the predicate devices.

Table of Performance (extracted from comparison tables):

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

This document does not describe a test set or data derived from human subjects. The testing described is non-clinical bench testing:

• Electrical safety tests
• Electromagnetic compatibility tests
• Performance tests (measuring output parameters against specifications)
• Software verification and validation
• Usability testing (presumably, for the device's user interface, not clinical user performance)

There is no mention of "country of origin for data" or "retrospective/prospective" as these are not relevant to bench testing for device parameters.

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 is not applicable. "Ground truth" in the context of AI or clinical diagnostic studies (e.g., for radiologists interpreting images) is not established or referenced here because this is a physical medical device (muscle stimulator) undergoing bench testing for safety and performance specifications, not an AI diagnostic tool.

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

Not applicable. There are no clinical or image-based test sets requiring adjudication by experts.

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 muscle stimulator, not an AI-assisted diagnostic tool. No MRMC study was performed or required for this type of submission.

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

Not applicable. There is no AI algorithm being cleared as a standalone device. The "software validation testing" mentioned (IEC 62304) relates to the safe and effective operation of the device's inherent control software, not an AI-driven clinical function.

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

Not applicable in the context of AI or clinical ground truth. For this submission, "ground truth" implicitly refers to the engineering specifications and established safety limits as defined by relevant IEC standards and comparison with predicate devices. The device's output parameters were measured against these defined standards and specifications.

8. The sample size for the training set

Not applicable. This is a medical device, not an AI model. There is no training set for an AI algorithm mentioned or required.

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

Not applicable. As above, there is no AI training set.

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