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The Anorectal Analysis Module is a software program that has been designed to analyze pressure data recorded from the lower gastrointestinal tract in pediatric and adult populations.

The Polygram Software for Windows (K946322) was designed to record and handle/store physiological parameters. After a recording, the user is able to review the tracings on the computer screen and print the signal tracings or just parts of them. When reviewing the data on the computer screen, the user is able to mark certain segments and calculate certain parameters from the selected signal segments. These parameters include minimum and maximum values, length of selection. By adding the Anorectal Analysis Module to the Polygram Software for Windows, the user can also have all the pressure data analyzed in terms of physiological properties, comparison with normal values, etc. The analyzed data can thereafter be viewed on screen or printed out on a separate paper. The analysis report includes sections such as patient demographics; interpretation and comment (for user to insert); procedure summary; Anorectal tracing; analyses (as specified below); and physician signature section.

The provided 510(k) summary for the "Anorectal Manometry Analysis Module" (K972439) describes the device and its intended use, but it does not contain detailed information about specific acceptance criteria or a formal study proving the device meets those criteria, as typically understood in a clinical performance study for AI/ML devices.

Instead, the summary focuses on software validation and equivalence to a previously marketed DOS-based software. The "Performance Testing" section describes a typical software development life cycle's testing phases rather than a clinical performance study.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Given the nature of the submission (a software update/equivalence 510(k) from 1997), the acceptance criteria described are primarily related to software functionality and successful internal/external testing. Clinical performance metrics (e.g., sensitivity, specificity, AUC) are not mentioned.

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

• Sample Size: Not specified. The document mentions "hospital site testing" (beta testing) but does not provide details on the number of cases or patients used for this testing.
• Data Provenance: Not specified. It's implied that the beta testing would involve patient data from the participating "hospital site," but details on country of origin or whether the data was retrospective or prospective are absent.

3. Number of Experts Used to Establish Ground Truth and Qualifications

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. The document states the device is "under supervision of a trained physician," implying physician involvement in the use and evaluation during beta testing, but it doesn't detail their qualifications for establishing ground truth for the software's analytical results.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified. Given the era and the type of device (software for analyzing physiological data), formal multi-expert adjudication methods typically seen in image-based AI/ML devices are unlikely to have been detailed or even employed in the same manner. The "beta testing" likely involved clinical users validating the software's output against their own clinical assessment and expected results.

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

• MRMC Study: No. There is no mention of an MRMC study comparing human readers with and without AI assistance. The device is an analysis module rather than an AI-assisted diagnostic aid in the modern sense.

6. Standalone Performance Study (Algorithm Only)

• Standalone Performance Study: Yes, in the context of software validation. "Alpha testing in terms of integration testing" and "beta testing" implicitly covered the standalone performance of the software's analytical functions. However, this is not a "standalone performance" in the sense of a clinical trial demonstrating diagnostic accuracy (e.g., sensitivity, specificity) of the algorithm without human oversight. It's about ensuring the software's calculations and reports are accurate and consistent.

7. Type of Ground Truth Used

• Type of Ground Truth: The document does not explicitly state the type of ground truth used. For this type of device, ground truth would likely refer to:
  • Predicate Device Output: Verifying that the Anorectal Manometry Analysis Module generates the same analytical results as the predicate Polygram Software (DOS) for the same input data.
  • Expected Physiological Principles/Clinical Judgement: During beta testing, clinical users (physicians) would likely assess if the analyses align with established physiological principles and their clinical expectations for the recorded pressure data.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not applicable in the context of AI/ML. The "Anorectal Manometry Analysis Module" is described as software that performs predefined analyses (e.g., Vector Volume Analysis, Automatic Base Line Analysis). It is not an AI/ML device that learns from a training set to perform its functions. Its functions are deterministic based on programmed algorithms. Therefore, there is no "training set" in the machine learning sense.

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

• How Ground Truth for Training Set Was Established: Not applicable, as there is no training set for this type of deterministic software. The algorithms for analysis would typically be based on established medical and physiological principles of anorectal manometry.

In summary, the 510(k) for the Anorectal Manometry Analysis Module (K972439) focuses on software validation and substantial equivalence to a predicate device rather than clinical performance studies demonstrating diagnostic accuracy with specific quantitative criteria and detailed ground truth establishment, as would be expected for a modern AI/ML medical device submission.

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