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Pixie Skin Taq is a pen-like cryogenic device that utilizes pressurized nitrous oxide (NoO, liquefied) to destroy skin tag tissue. The N2O is released into a single-use, disposable applicator, cooling the applicator as the result of rapid evaporation. The cooled applicator is briefly contacted with the skin tag while the surrounding tissue is protected by a disposable thermally insulating adhesive bandage. Each Pixie Skin Tag device is supplied in a carton with instructions for use and replacement applicator tips and insulating adhesive bandages.

AI/ML Overview

The provided text is a 510(k) summary for the Pixie Skin Tag device. This type of submission is for establishing substantial equivalence to a predicate device, not for proving a device meets specific performance acceptance criteria via a rigorous clinical study of AI performance, especially not as would be described for an AI/ML-driven medical device.

Therefore, the information required to answer your questions regarding acceptance criteria, sample sizes for test/training sets, expert qualifications, ground truth establishment, MRMC studies, or standalone algorithm performance, as typically found in submissions for AI/ML devices, is not present in this document.

This document describes a physical medical device (a cryosurgical unit) and its comparison to a predicate device. The performance evaluations cited are limited to:

Bench studies: Comparison of applicator temperatures, device robustness (activation, N2O evacuation, drop protection), and thermal protection of insulating bandages.
Animal studies: Application to healthy rabbit skin to determine epithelial damage and wound healing.
Usability studies: Novice laypersons' ability to set up and apply simulated treatments after reading instructions.
Post-market review of complaints: For CE-marked devices and a reference Pixie device for warts, to assess safety and reported adverse events.

In summary, this document does not contain the information needed to answer the specific questions about acceptance criteria for an AI/ML device.

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K Number

K201366

Device Name

Forwarts

Manufacturer

Pronova Laboratories BV

Date Cleared

2021-01-21

(244 days)

Product Code

GEH

Regulation Number

878.4350

Type

Traditional

Panel

General and Plastic Surgery (SU)

Reference & Predicate Devices

K172373,K140314,K032271,K130599

Intended Use

Forwarts® is indicated for the treatment of warts on hands (common warts) and feet (plantar warts).

Device Description

Forwarts® is a wart removal device that works by the principle of cryotherapy and is intended for the treatment of common and plantar warts. It delivers a precise cryogenic spray directly onto a wart or verruca which causes them to freeze and fall off the skin in approximately 10-14 days. The device consists of a pressurized aerosol can containing 35 ml dimethyl ether (DME). The aerosol can is held in a polypropylene shell (front and back). The front shell contains a distance holder for a safe and regulated application and a visor to point and limit the spray pattern. The translucent back shell contains a ridge for safe handling. The valve system will be extended by an actuator to point and minimize the spray pattern. Forwarts® is supplied with ten (10) protection patches per device to be placed around the wart prior to treatment to help prevent damage of healthy skin. Forwarts® is supplied non-sterile and not intended to be sterilized by the end user. The Forwarts® nozzle should be cleaned after each use with rubbing alcohol (70%).

AI/ML Overview

The provided document describes the "Forwarts®" device, a cryosurgical unit for wart removal. Here's a breakdown of the acceptance criteria and the study information based on the text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document primarily focuses on demonstrating substantial equivalence to predicate devices rather than listing specific, quantified acceptance criteria with corresponding performance data in a direct tabular format. However, based on the non-clinical performance data section, we can infer the acceptance criteria and the device's reported performance against them.

Acceptance Criterion (Inferred)	Reported Device Performance
Biocompatibility: Device materials in contact with the patient should be biocompatible.	Supports biocompatibility of the device, conforming to ISO 10993-1 (cytotoxicity, irritation, sensitization, toxicological risk assessment, chemical characterization). No carcinogenicity testing required based on TRA.
Shelf Life: Device should maintain stability and functionality for a specified duration.	Supports shelf life of twenty months. Demonstrated stability for twenty months under worst-case simulated conditions per ASTM D3090-72.
Transportation Integrity: Packaging should protect the device during transport.	Demonstrates package integrity maintained. Subjected to impact, drop, vibration, and low-pressure testing under simulated transport conditions per ASTM D4169, showing suitability of packaging.
Thermal Performance: Achieve comparable freezing characteristics to predicate devices.	Supports device performance. Comparative thermal performance testing (thermoelectric and simulated skin model) showed comparable performance to the predicate devices regarding freeze temperature extreme, plateau, duration, cycle length, and spot size. Achieved thermal performance characteristics within the ranges of the predicates, demonstrating comparable efficacy and no new questions of safety.
Self-selection/Label Comprehension: Lay-users can correctly self-select and understand labeling.	Supports device self-selection and labeling comprehension with a success rate of at least 90% when used as indicated.
Human Factors (Usability): Safe usability for lay-person use.	Supports safe usability of the device for lay-person use. Formative Study (Simulated Use Testing, Contextual Enquiry, semi-guided interview) and Human Factor Validation Test validated safe intended use.
Substantial Equivalence: Maintain similar safety and effectiveness profile as predicate devices.	Substantially equivalent to the predicate device. Same intended use, same or similar technological characteristics. Performance testing demonstrated similar safety and effectiveness, raising no new or different questions of safety and effectiveness.

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

The document does not explicitly state a sample size for the "test set" in the context of clinical trials or specific data points. The studies mentioned are:

Biocompatibility: General testing per ISO 10993 standards. No specific sample size for a "test set" mentioned; likely refers to laboratory testing of materials.
Shelf Life: Testing of device stability and functionality under simulated conditions. No specific sample size for a "test set" mentioned.
Transportation Testing: Testing of the device in its final shipping configuration. No specific sample size for a "test set" mentioned.
Bench Testing (Thermal Performance): Thermoelectric testing and simulated skin model. No specific sample size for a "test set" mentioned; likely refers to laboratory measurements.
Self-selection / Label Comprehension Study: "Study subjects were provided with the Forwarts® labeling." No specific sample size provided, nor directly stated provenance (e.g., country of origin, retrospective/prospective).
Human Factors Study: "Formative Study... included Simulated Use Testing... and a semi-guided interview with test subjects. A Human Factor Validation Test was then performed." No specific sample size provided, nor directly stated provenance.

It is important to note that this submission is a 510(k) premarket notification, which relies on demonstrating substantial equivalence to legally marketed predicate devices rather than requiring extensive clinical trials with large test sets typically seen for PMA submissions. The studies mentioned are primarily non-clinical or usability studies.

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

The document does not describe the use of experts to establish ground truth for any test sets. The studies mentioned (biocompatibility, shelf life, transportation, thermal performance, self-selection, human factors) do not typically involve expert consensus for ground truth in the way a diagnostic AI device would, where expert radiologists/pathologists review cases.

4. Adjudication Method for the Test Set:

No adjudication method is mentioned as there is no specific "test set" and ground truth establishment by experts as would be seen in a clinical trial for a diagnostic device.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

No MRMC comparative effectiveness study was done. The device is a cryosurgical unit, not a diagnostic AI device that would typically involve human readers.

6. Standalone Performance (Algorithm Only without Human-in-the-Loop Performance):

This question is not applicable to the Forwarts® device, as it is a cryosurgical unit and not an algorithm or AI system.

7. Type of Ground Truth Used:

The "ground truth" for the various non-clinical and usability studies is inherent in the methodology:

Biocompatibility: Established by adhering to ISO 10993 standards.
Shelf Life: Determined by stability and functionality over time under simulated conditions.
Transportation Testing: Demonstrated by maintaining package integrity and device functionality after simulated transport.
Thermal Performance: Measured physical characteristics (temperature, duration, size of freeze spot) with comparative data against predicate devices.
Self-selection / Label Comprehension: Success rate based on participant actions and comprehension during the study.
Human Factors Study: Validation of safe usability based on observations during simulated use and interviews.

8. Sample Size for the Training Set:

This question is not applicable as the Forwarts® device is a physical cryosurgical unit and not an AI/ML algorithm that requires a training set.

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

This question is not applicable as the Forwarts® device is a physical cryosurgical unit and not an AI/ML algorithm that requires a training set.

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