(189 days)
The PrefTest Professional Suite™ is used to measure penile tumescence in response to visual and auditory stimuli. Penile Tumescence data can later be combined with other standardized psychological testing procedures to assist clinicians with their assessment and treatment of sexual offenders.
Limestone Technologies Penile Plethysmograph is based on a circumference measurement using a strain gauge transducer which measure penile tumescence in millimeters. PrefTest Professional Suite™ computer software is used to store, tabulate, display and print out the acquired data from a personal computer or lap top.
This 510(k) summary for the PrefTest Professional Suite™ indicates that its clearance was based on substantial equivalence to a predicate device, the MONARCH 21 Penile Plethysmograph (K033126), rather than on specific quantitative acceptance criteria or a detailed clinical study demonstrating its performance against such criteria.
Here's a breakdown of the available information based on your requested categories:
1. A table of acceptance criteria and the reported device performance
Based on the provided text, there are no explicit quantitative acceptance criteria or a table of performance metrics. The document states:
- "Testing for the PrefTest Professional Suite™ software included software validation."
- "A risk analysis performed according to ISO 14971: 2000 was conducted."
- "Testimonials and published literature supports the safety and effectiveness of this technology."
This suggests that the device's performance was assessed through software validation and risk analysis, and its safety and effectiveness were supported by existing evidence for the technology rather than a new comparative study with specific acceptance thresholds.
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
Not applicable. The submission does not describe a clinical test set with a specific sample size.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
Not applicable. There is no mention of an expert panel or ground truth establishment for a specific test set.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable. There is no mention of an adjudication process for a test set.
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 a measurement instrument (penile plethysmograph) and associated software, not an AI-assisted diagnostic tool that would typically involve a multi-reader multi-case study with human readers.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This concept doesn't directly apply. The device's function is to measure penile tumescence, store, tabulate, display, and print data. Its "performance" would likely relate to the accuracy and reliability of its measurements, and the proper functioning of its software. The text indicates "software validation" was performed, which is a form of standalone testing for the software's functionality, but no specific performance metrics are provided.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
The document does not detail specific ground truth for performance testing. For a measurement device, the "ground truth" for validation would typically involve comparing its measurements against a known standard or a reference measurement system. The text mentions "software validation" and "risk analysis," which imply internal testing and adherence to standards, but no external ground truth against which the device's measurements were compared.
8. The sample size for the training set
Not applicable. This device is not an AI/ML model that would typically have a "training set" in the context of machine learning.
9. How the ground truth for the training set was established
Not applicable, as there is no training set for an AI/ML model.
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