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The BACK 4 device employs RF technology or EMS technology for the treatment of selected medical conditions.

The BACK 4 device in RF mode is intended to provide topical heating for the purpose of elevating tissue temperature for the treatment of selected medical conditions such as relief of pain, muscle spasms, and increase in local circulation. The BACK 4 massage device is intended to provide a temporary reduction in the appearance of cellulite.

The BACK 4 device in EMS mode is intended for:
*Prevention or retardation of disuse atrophy
*Increasing local blood circulation
*Muscle re-education
*Maintaining or increasing range of motion.

The RF treatment mode and EMS mode should not be used in combination or sequentially.

The BACK 4 device generates high frequency sinusoidal current with a monopolar mode of application using two electrodes. A neutral electrode is placed in contact with the patient and a handheld active electrode is manipulated by a therapist. When both electrodes are in contact with a patient the electrical circuit is closed and RF therapy can be provided. The device can be operated in a capacitive or resistive monopolar modes and a multipolar mode.

The product consists of a power console, LCD monitor, and accessories including capacitive, resistive and multipolar electrodes. The unit can be adjusted to provide various levels of treatment frequency ranging from 300kHz to 1MHz.

The product can also employ EMS technology. It generates a 4kHz or 1.5kHz electrostimulation signal (modulated at a frequency set between 2Hz and 200Hz).

The two RF and EMS technologies should not be used in combination or sequentially.

An Emergency stop button feature allows the patient to shut down the unit in the event of any discomfort.

The provided text describes the regulatory clearance of a medical device (BACK 4) through a 510(k) premarket notification. The core of a 510(k) submission is demonstrating substantial equivalence to a legally marketed predicate device. This typically involves showing that the new device is as safe and effective as the predicate, often through performance testing.

However, the provided document focuses heavily on comparing the technical characteristics and intended uses of the BACK 4 device to two predicate devices (BACK3 COLOR and Evolve System with T3 Applicator). It lists various performance parameters and notes where the BACK 4 is identical or differs from the predicates, consistently concluding that these differences do not raise new questions of safety and effectiveness because the device underwent required performance testing and validation.

Crucially, the document explicitly states that performance testing was done, but does not provide specific acceptance criteria for these tests or detailed results that quantify the device's performance against said criteria. It highlights that tests were conducted according to relevant IEC standards for electrical safety, EMC, and software validation, and that a "tissue temperature elevation study" was performed for the RF mode.

Given this, I can only construct the table and address the other points based on the inference from the document that these tests were performed and deemed satisfactory for substantial equivalence, rather than providing concrete numerical acceptance criteria and reported numerical performance values. The document asserts that performance was "satisfactory" and "demonstrated conformity," implying that the device met internal or standard-driven criteria.

Here's a breakdown based on the provided text, addressing your points where information is available:

Acceptance Criteria and Device Performance

Due to the nature of this 510(k) summary (focusing on substantial equivalence to predicates rather than presenting de novo clinical trial data with explicit performance metrics), specific numerical acceptance criteria and reported device performance values are not provided in the document. The document primarily asserts that the device met its design requirements and applicable standards, implying that it met the necessary performance thresholds established for substantial equivalence to the predicate devices.

Table 1. Acceptance Criteria and Reported Device Performance (Inferred from Document)

Study Details:

1. Sample sizes used for the test set and the data provenance:

   • Test Set (Human Being Testing): The "tissue temperature elevation study" for the RF mode was conducted on "three different people on three body parts."
   • Data Provenance: Not explicitly stated, but given the company (SWIMS America Corp, White Plains, NY), it's likely US-based or conducted to US regulatory standards. The study type is prospective as it involved actual testing on human subjects for the purpose of demonstrating performance.

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

   • The document does not specify the number or qualifications of experts involved in establishing ground truth for the "human being testing" or any other performance studies. The ground truth for the human testing appears to be the directly measured skin and room temperatures and the clinical observation of whether therapeutic temperatures were maintained.

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

   • The document does not provide information on any adjudication methods. The performance testing described (electrical safety, EMC, biocompatibility, software validation, human temperature study) likely involves objective measurements against predefined criteria/standards rather than subjective assessments requiring adjudication.

4. 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 device is a powered muscle stimulator/RF heating device, not an image analysis AI device that would typically involve human "readers" or AI assistance for diagnostic tasks.

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

   • This question is not directly applicable as the device is a therapeutic physical medicine device, not an algorithm-only diagnostic or analytical tool. Its performance is inherent in its physical outputs (RF heating, EMS signals) as applied to a patient, rather than an "algorithm-only" output in the sense of an AI diagnostic model. However, the performance parameters (e.g., RF frequency, output power, EMS specifications) are outputs of the device's internal algorithms and hardware configurations, and these were assessed in the various safety and performance tests.

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

   • For the RF mode's topical heating performance, the ground truth was direct physiological measurement (skin and room temperatures) against a defined therapeutic range (38-42°C).
   • For electrical safety, EMC, and biocompatibility, the ground truth was adherence to established international standards (IEC 60601-1, IEC 60601-1-2, ISO 10993-1), verified through objective testing.
   • For software assessment, the ground truth was compliance with FDA software validation guidelines.
   • For usability and risk management, the ground truth was adherence to predefined worst-case assumptions and risk analysis principles.
   • For the EMS mode, the ground truth for parameters like burst characteristics was compliance with FDA Powered Muscle Stimulator Guidance.

7. The sample size for the training set:

   • This information is not applicable/provided as this is a medical device clearance, not an AI/machine learning model where "training sets" are explicitly discussed. The device's design and operation are based on engineering principles and regulatory standards, not on a machine learning training process.

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

   • Not applicable, as there is no "training set" in the context of this device's development as described. The device's design and verification relied on established engineering standards, predicate device characteristics, and performance testing data.

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