Search Results

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.

Ask a specific question about this device

The R2 Dermal Cooling System is a cryosurgical instrument intended for use in dermatologic procedures for the removal of benign lesions of the skin.

The R2 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 the user interface, the system controller, and the power source
• handpiece – contains the TEC, thermistors, and the aluminum cooling plate in a thermoplastic housing
• chiller - provides circulating water to the handpiece to remove heat extracted by the TEC
• isolation transformer – isolates system from AC power

Here's a breakdown of the acceptance criteria and study information for the R2 Dermal Cooling System, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not explicitly stated for specific tests beyond "multiple times" for usability testing. For the cryoablation zone equivalence, an "in vitro simulated model" was used.
• Data Provenance: All testing appears to be retrospective bench testing and usability testing conducted by the manufacturer, R2 Dermatology, Inc. There is no indication of country of origin for the data; it's assumed to be domestic (USA) given the FDA submission.
• No preclinical or clinical testing was performed.

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

• Not applicable. The document does not describe the use of human experts to establish ground truth for the test set. Ground truth was established through technical specifications, bench testing, and comparison with a predicate device in an in vitro model.

4. Adjudication Method for the Test Set

• Not applicable. There was no explicit adjudication method described as there were no human-read studies or subjective assessments requiring adjudication. The performance was assessed against predefined technical specifications and comparison to a predicate device via objective measurements.

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 device is a cryosurgical instrument, not an AI-assisted diagnostic tool. Therefore, an MRMC comparative effectiveness study involving human readers and AI assistance is not applicable and was not performed.

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

• Yes, in a sense. The core "performance data" and "substantial equivalence testing" were standalone in that they evaluated the device's physical and functional capabilities (thermal performance, accuracy, software operation) and its ability to create a cryoablation zone equivalent to a predicate in an in vitro model, without a human operator's diagnostic or interpretative input being part of the primary performance metric. The usability testing, while involving humans, focused on their ability to operate the device, not on their diagnostic effectiveness.

7. The Type of Ground Truth Used

• Bench Test Specifications: For thermal performance, accuracy, physical specifications, power control, etc., the ground truth was the pre-defined engineering and design specifications of the device.
• Predicate Device Performance: For the cryoablation zone equivalence, the ground truth was the established performance of the predicate CryoPen® Surgical System in creating a cryoablation zone, as measured in an in vitro model.
• Regulatory Standards: For biocompatibility, electrical safety, EMC, and usability, the ground truth was compliance with recognized international and FDA standards (ISO-10993, IEC 60601 series, FDA software validation guidance).

8. The Sample Size for the Training Set

• Not applicable. This document describes the testing and validation of a physical medical device, not a machine learning model. Therefore, there is no "training set" in the context of AI/ML.

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

• Not applicable. As there is no training set mentioned or implied for an AI/ML model, the establishment of ground truth for such a set is not relevant to this document.

Ask a specific question about this device