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The InMode System with the Morpheus8 (Fractora) Applicators is intended for use in Dermatological and General Surgical procedures for Electrocoagulation and Homeostasis.

At higher energy levels greater than 62 ml/pin, use of the Morpheus8 (Fractora) Applicators is limited to Skin Types I-V.

The InMode System with the Morpheus8 (Fractora) Applicators is a computerized, programmed, RF technology-based device intended for dermatological applications which requires skin electrocoagulation and hemostasis.

The device platform is basically constituted on the same system platform as FDA cleared for the proposed predicate device; The InMode System with the Fractora3D/3D-90 Applicators (K180189). The device applicators employ fractional RF multi-electrode technology for procedures requiring electrocoagulation and hemostasis. The Morphues8 (Fractora) Applicators are designed to deliver radiofrequency energy to the skin in a fractional manner, via an array of multielectrode pins.

The Device provides enhanced safety while minimizing possible side effects by monitoring RF parameters. The InMode System with the Morpheus8 (Fractora) Applicators consists of an AC/DC power supply unit, RF generator, controller and user interface including LCD touch screen. The Morpheus8 (Fractora) Applicators are connected to the console via a cable and a foot switch activates the energy delivery to the applicator. The subject device applicator comprises a disposable, single use, fractional RF electrode pins tip head.

The InMode System with the Morpheus8 (Fractora) Applicators comprises the following applicator tip heads:

• Morpheus8 24 Pin Applicator (Fractora3D) tip (FDA Cleared in K180189)
• Morpheus8 40 Pin treatment tip (New tip) .
• . Morpheus8 12 Pin treatment tip (New tip)
• . Morpheus8 T Pin treatment tip (New tip)

The provided document is a 510(k) Premarket Notification from the FDA for a medical device called the "InMode System with the Morpheus8 (Fractora) Applicators." It establishes the substantial equivalence of this new device to a previously cleared predicate device.

Crucially, this document is for an electrosurgical cutting and coagulation device, not an AI/ML-based medical device. Therefore, the questions related to AI/ML device performance, such as ground truth, multi-reader multi-case studies, training sets, and expert consensus, are not applicable to the information contained in this document.

The primary acceptance criteria for this type of device, as per the document, revolve around substantial equivalence to a predicate device in terms of:

1. Intended Use/Indications for Use
2. Technological Characteristics
3. Safety and Effectiveness (demonstrated through performance testing and compliance with recognized standards).

Here's a breakdown of the relevant information provided in the document concerning the device's acceptance criteria and the study proving it meets those criteria:

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by demonstrating substantial equivalence to the predicate device (InMode System with the Fractora3D/3D-90 Applicators, 510(k) No. K180189). The 'studies' are non-clinical bench performance tests.

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

• Sample Size for Test Set: Not applicable in the traditional sense of a clinical or image-based AI/ML test set. The 'test set' here consists of the subject device (InMode System with new Morpheus8 applicators) comparing its performance and characteristics against the predicate device.
• Data Provenance: The tests are non-clinical bench performance tests. Data is proprietary to the manufacturer (InMode MD Ltd.) and was collected as part of their device development and regulatory submission process. There is no mention of country of origin for data related to patients or clinical studies, as none were required or performed. The tests are "retrospective" in the sense that they were performed after the device was manufactured to verify its specifications.

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

• This is not applicable as there is no "ground truth" established by experts in the context of an AI/ML device for diagnostic purposes. The ground truth for performance here is the physical output of the device and its compliance with engineering specifications and recognized safety standards, measured by calibrated equipment.

4. Adjudication Method for the Test Set

• Not applicable for this type of device. Adjudication methods (e.g., 2+1, 3+1) are used for resolving disagreements among human readers or labelers, typically in image interpretation for AI/ML ground truth generation. Here, the "test" is largely a comparison of engineering specifications and performance with those of a predicate device and relevant standards.

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

• No. This type of study is for evaluating human performance, often with and without AI assistance, especially in diagnostic imaging. It is not relevant for an electrosurgical device like the InMode System.

6. If a Standalone (i.e., Algorithm Only Without Human-in-the-Loop Performance) was Done

• Not applicable. There is no "algorithm" in the sense of an AI/ML model being assessed independently. The performance assessed is the device's physical output and adherence to safety standards.

7. The Type of Ground Truth Used

• The "ground truth" implicitly used for this device's performance evaluation comprises:
  • Engineering Specifications: The design parameters and operational capabilities of the device as per its intended function (e.g., RF output power, frequency, energy per pin, dimensions).
  • Recognized Consensus Standards: Compliance with international standards for medical electrical equipment (IEC 60601 series, AAMI/ANSI standards) and sterilization (ISO 11137-1).
  • Predicate Device Performance: The established performance and safety characteristics of the legally marketed predicate device (K180189) serve as the benchmark for substantial equivalence.

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. As no training set is involved, no ground truth needed to be established for it.

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