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The JOULE diVa Laser Device with its accessories is intended for coagulation, ablation, or cutting of soft tissue (skin) in Dermatology, Plastic Surgery, ENT, Gynecology, General Surgery, Podiatry, and Ophthalmology (skin around the eyes).

The JOULE diVa Laser System, when used with its micro beam handpieces, is intended for use in Dermatological procedures and Skin resurfacing procedures.

The JOULE diVa Laser Device consists of a console and laser deliver accessories. It uses focusing optics to deliver optical energy to the treatment site. The control console houses the power supply, cooling system, articulated arm delivery system and/or fiber optic arm delivery system with a handpiece. The user activates laser emission by means of a footswitch.

The provided document is a 510(k) summary for the Sciton JOULE diVa Laser Device, which is a medical device and not an AI/ML-driven device. Therefore, the information requested about acceptance criteria, study details, sample sizes, ground truth establishment, expert adjudication, and MRMC studies, which are typically relevant for AI/ML device evaluations, is not applicable to this submission.

The document primarily focuses on demonstrating substantial equivalence to predicate devices through technical characteristics, intended use, and non-clinical performance (safety).

Here's a breakdown of the information provided in the document from the perspective of non-AI device validation, aligning with the sections of your request where applicable:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't present a table with specific acceptance criteria directly alongside performance metrics in the way one would for an AI model's accuracy, sensitivity, or specificity. Instead, it demonstrates performance by showing compliance with recognized consensus standards for safety and electrical characteristics.

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

Not applicable in the context of an AI/ML device validation. The performance evaluation here relies on standardized testing protocols (e.g., electrical safety tests, EMC tests, biocompatibility tests), not a "test set" of data in the AI sense.

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

Not applicable. Ground truth as typically defined for AI model validation (e.g., expert-labeled data) is not relevant for this type of device submission. Compliance with standards and successful completion of physical and software tests are the "ground truth" here, established by accredited testing bodies and internal quality controls.

4. Adjudication Method for the Test Set:

Not applicable. There is no "test set" or human-driven adjudication process described for the device's technical performance in the AI/ML context.

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

Not applicable. The JOULE diVa Laser Device is a hardware-based medical device for surgical procedures, not a diagnostic or AI-powered imaging interpretation tool that would typically undergo MRMC studies.

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

Not applicable. This is not an algorithm-only device. Its performance is evaluated through its physical and electrical characteristics and compliance with safety standards.

7. The Type of Ground Truth Used:

The "ground truth" for this device's performance is established by:

• Compliance with recognized consensus standards: This includes electrical safety (IEC 60601-1, IEC 60601-2-22), electromagnetic compatibility (IEC 60601-1-2), and usability (IEC 60601-1-6).
• Biocompatibility testing: Performed according to international standards (ISO 10993-5, ISO 10993-10, ISO 10993-23) to assess cytotoxicity, irritation, and sensitization.
• Software verification and validation: Demonstrated against FDA guidance for medical device software.

8. The Sample Size for the Training Set:

Not applicable. This device does not use an AI/ML model that requires a training set.

9. How the Ground Truth for the Training Set was Established:

Not applicable. As noted, there is no AI/ML training set for this device.

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