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510(k) Data Aggregation
(18 days)
Non-ablative treatment of mild to moderate facial wrinkles and rhytides for skin phototypes I-IV
Non-ablative treatment of mild to moderate facial wrinkles for skin phototypes I-IV
The purpose of this submission is to modify the Cleaning / Disinfection methods of the Non-Ablative Wrinkle Treatment Handpiece cleared as Ellman Non-Ablative Wrinkle Treatment Handpiece under K101967. The device is identical to the device cleared under K101967 Ellman International Non-Ablative Wrinkle Treatment Handpiece except for the modified Cleaning/Disinfection method.
This 510(k) summary describes a modification to a medical device, specifically changes to its cleaning and disinfection methods, rather than an AI-powered diagnostic or treatment device. Therefore, many of the requested categories related to AI performance, such as sample sizes for test sets and training sets, expert qualifications, adjudication methods, MRMC studies, and ground truth types, are not applicable in this context.
The document discusses the device's cleaning and disinfection methods and a study conducted to demonstrate their effectiveness.
1. Table of Acceptance Criteria and Reported Device Performance
Acceptance Criteria | Reported Device Performance |
---|---|
Achieves adequate log reduction of inoculated challenge organisms on test devices with the proposed cleaning/disinfection method. | "Ellman International has presented a successfully executed test protocol... to demonstrate that the methods described above achieve an adequate log reduction of inoculated challenge organisms on the test devices." |
Provides an effective alternate cleaning and disinfection to the existing instructions for use. | "Ellman International has presented substantial evidence to support the claims made in method above to provide an effective alternate cleaning and disinfection to the existing instructions for use." |
2. Sample size used for the test set and the data provenance
The document does not specify the sample size (e.g., number of test devices or samples) for the test set used in the cleaning/disinfection study. The data provenance is described as "test protocol as part of this submission" and "SGS Protocol # 09A1006547 and demonstrated in SGS Report# 09A1006548," indicating an internal or contracted laboratory study rather than clinical data from a specific country or retrospective/prospective collection.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable. The study is a laboratory test of cleaning and disinfection efficacy, not an assessment of diagnostic performance requiring expert interpretation.
4. Adjudication method for the test set
Not applicable. This is a laboratory test, not a diagnostic assessment with human readers.
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 device is not an AI-powered diagnostic tool, and no MRMC study was conducted.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable. This is a medical device for non-ablative wrinkle treatment, not an algorithm. The study focuses on the efficacy of a cleaning and disinfection protocol.
7. The type of ground truth used
For the cleaning and disinfection study, the "ground truth" would be the direct measurement of bacterial/microbial reduction on the test devices after applying the proposed method. This is laboratory-derived quantitative data, not expert consensus, pathology, or outcomes data in the typical sense of AI-driven diagnostics.
8. The sample size for the training set
Not applicable. This document describes a traditional medical device and its cleaning protocol validation, not an AI/ML system requiring a training set.
9. How the ground truth for the training set was established
Not applicable. As there is no training set mentioned, the ground truth establishment is irrelevant in this context.
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