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The Massage Compression Boots are intended for the temporary relief of minor muscle aches and for the temporary increase in circulation to the treated areas. The device can simulate kneading and stroking of tissues by using an inflatable garment.

The subject device is a portable and rechargeable device. It is intended to be an over-the-counter portable inflatable tube massage system which simulates kneading and stroking of tissue by use of an inflatable garment. It can be used to temporarily increase blood circulation and temporarily relieve minor muscle aches and pains. The device, supplied clean and non-sterile, utilizes the pneumatically controlled chambers actuated by an electronically controlled air pump unit. All pump, battery and control components are protectively housed in a plastic case of the control unit that is permanently attached to an inflatable sleeve of multiple air chambers. An ON/OFF button, a SET button, and a display of LEDs provide for user interface. There is also a port for connecting the battery charger/AC adapter plug. The sleeve component consists of multiple air chambers/bladders encased inside a soft medical fabric or equivalent medical for increased patient comfort and biocompatibility compliance.

The provided text is a 510(k) summary for the JKH USA, LLC Massage Compression Boots. This document focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed acceptance criteria and the results of a standalone study proving the device meets specific performance criteria.

Therefore, many of the requested details about acceptance criteria, test set characteristics (sample size, provenance, expert consensus, adjudication), multi-reader multi-case studies, and ground truth for training/testing are not present in this type of regulatory submission. The FDA 510(k) pathway for Class II devices often relies on demonstrating that a new device is as safe and effective as a legally marketed predicate device, rather than requiring extensive clinical trials with pre-defined performance metrics like sensitivity, specificity, and reader studies that are common for AI/ML-driven diagnostic devices.

However, I can extract the information that is available from the document:

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

The document doesn't present a table of acceptance criteria and reported device performance in the typical sense of quantitative metrics for a new, often AI/ML-driven, medical device's diagnostic or predictive capabilities. Instead, it compares the subject device to predicate devices on various technical specifications to demonstrate substantial equivalence.

Here's a summary of the comparisons, which serve as the "performance" relevant to a 510(k) for this type of device:

2. Sample sized 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 clinical "test set" in the context of an AI/ML algorithm's performance evaluation. The substantial equivalence argument is based on non-clinical tests (bench tests for physical, electrical, and performance requirements, and software verification) and comparison to predicate devices, not patient data from a test set. Therefore, sample size and data provenance are not applicable in this context.

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. As noted above, there was no clinical "test set" requiring expert-established ground truth for performance evaluation of a diagnostic or predictive algorithm.

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

Not applicable. No test set requiring adjudication was used.

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 not mentioned or required for this 510(k) submission for a non-diagnostic pneumatic compression device.

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

This is not an AI/ML diagnostic algorithm. The device itself is a "Powered Inflatable Tube Massager." Its "performance" is demonstrated by meeting general safety standards and comparison of its physical and functional characteristics to legally marketed predicate devices, not by a standalone performance study in the way an AI algorithm would be evaluated. The software verification was performed according to FDA guidance, but this refers to the robustness and functionality of the device's control software, not a standalone diagnostic performance.

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

Not applicable in the context of an AI/ML diagnostic or predictive model. The "ground truth" for this device's performance relies on its adherence to safety standards (e.g., electrical, mechanical) and the functional equivalence (e.g., pressure range, cycle time, intended use) to predicate devices already deemed safe and effective.

8. The sample size for the training set

Not applicable. There is no mention of an AI/ML training set as this is not an AI/ML device in the context of what would typically require such a set (e.g., for image analysis or disease prediction).

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

Not applicable.

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