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The Dermal Cooling System is a cryosurgical instrument intended for use in dermatologic procedures for the removal of benign lesions of the skin.

The Dermal Cooling System is a cryosurgical device used to cool the skin, without the use of cryogenic gases or liquids, for the removal of benign skin lesions. Surface contact cooling is achieved using a thermoelectric cooler (TEC), with an integrated aluminum plate, to lower the temperature of the skin. It is intended for use in a healthcare facility such as a clinic or doctor's office.

The Dermal Cooling System is comprised of the following components:

• . control unit – houses user interface display, the system controller, and the power converters
• . chiller – provides circulating coolant to the handpiece to remove heat from the TEC
• . handpiece - contains the TEC, temperature sensors, the aluminum cooling plate, and user interface elements
• . isolation transformer – isolates system from AC mains power
• . cart – houses the isolation transformer, chiller, and control unit

This document describes a 510(k) premarket notification for a Dermal Cooling System, not an AI/ML device. Therefore, the specific questions related to AI/ML device acceptance criteria, training sets, and ground truth establishment are not directly applicable to the provided information.

However, I can extract and structure the information related to the device's acceptance criteria and performance as presented in the document.

Device: Dermal Cooling System (Cryosurgical Unit and Accessories)

Indications for Use: The Dermal Cooling System is a cryosurgical instrument intended for use in dermatologic procedures for the removal of benign lesions of the skin. It is intended to be used by trained healthcare professionals.

Here's an analysis based on the provided document, addressing the relevant points:

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

2. Sample sized used for the test set and the data provenance:

• Sample Size:
  • For the usability testing, "individuals with varying degrees of experience" were used, including "novice and experienced users." The exact number is not specified.
  • For the in vitro simulated model for subsurface temperature testing, the sample size (number of tests or models) is not specified.
• Data Provenance: The tests were bench tests and in vitro simulations. There is no mention of country of origin for the data as it's not patient data. The study was performed to support a 510(k) submission, indicating a pre-market evaluation rather than a post-market study or retrospective analysis of clinical data. It is a prospective testing approach for regulatory approval.

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

This is a physical device, not an AI/ML system that requires expert-established ground truth in the same way. The "ground truth" for this device's performance is established by objective physical measurements (e.g., temperature, force, weight) and functional performance checks against engineering specifications (e.g., maintain temperature, cycle LEDs).

For usability testing, "individuals with varying degrees of experience" were used, but these were users (not necessarily experts establishing ground truth about the device's function, but rather testing its usability).

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

Not applicable. This is not an AI/ML system requiring image or data interpretation by multiple readers/experts. The tests described are objective engineering and performance validations.

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 is not an AI/ML device, and no MRMC study, human reader improvement, or comparative effectiveness with or without AI assistance was performed or is relevant.

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

Not applicable. This is a physical medical device. It has software control for its operation, but it's not a standalone diagnostic algorithm.

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

The "ground truth" for this device's performance is based on:

• Engineering Specifications: Designed performance parameters (e.g., specific temperatures, power output, weight limits).
• Physical Measurements: Objective measurements using validated sensors and equipment (e.g., force sensors, thermistors, calibrated controls).
• Functional Verification: Demonstrating the device performs its intended actions (e.g., LEDs cycle, buzzer activates, pre-cool/cooling/warming modes work).
• Simulated Model Performance: An in vitro simulated model was used to verify the creation of a cryoablation zone.

The document explicitly states: "No preclinical or clinical testing was performed." and "There were no changes to patient contacting material and, as such, biocompatibility testing was not repeated." This indicates reliance on bench testing and in-vitro models rather than patient outcomes or pathology.

8. The sample size for the training set:

Not applicable. This is not an AI/ML device; there is no "training set" in the context of machine learning. The device's software is rule-based control logic, not a machine learning model.

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

Not applicable, as there is no training set for an AI/ML model. The software's functionality is verified against design specifications and validated through testing (as noted in point 7). "Software verification and validation testing was conducted per FDA's 'General Principles of Software Validation; Final Guidance for Industry and FDA Staff' (January 2002)." This guidance focuses on traditional software engineering best practices, not machine learning training.

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