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The PENTAX Medical Auto Leakage Tester SHA-P6 is intended to perform leakage testing on PENTAX Medical flexible endoscopes.

PENTAX Medical Auto Leakage Tester SHA-P6 (Hereinafter "the Device") is intended to be used to perform leakage testing on PENTAX Medical flexible endoscopes are complex devices that consist of various mechanical, optical, and video elements that might be affected by exposure to fluid. Therefore, endoscope leak testing is a crucial procedure for the proper endoscope cleaning and disinfecting process. The Device is designed to detect leaks by measuring the difference in air pressure over time using the Automated Leak Test mode (ALT mode). The Device conducts two types of tests: a dry leakage test and a wet leakage test. The dry leakage test is performed when the device feeds air into the endoscope until a set pressure is reached, and then monitors the pressure over a specific period of time. The wet leakage test is conducted after confirming the absence of a leak during the dry leakage test, by immersing the endoscope in potable water to test for loss of integrity in its watertight construction. The Device is compatible with PENTAX Medical flexible endoscopes.

Given the provided text, the PENTAX Medical Auto Leakage Tester SHA-P6 is a leakage tester for endoscopes, not an AI/ML device. Therefore, typical metrics related to AI/ML such as sensitivity, specificity, AUC, human reader improvement, and ground truth establishment by experts are not applicable here. The document describes a traditional medical device submission (510(k)) where substantial equivalence is demonstrated through performance testing against a predicate device.

Here's an analysis based on the information available, addressing the applicable points:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly state numerical acceptance criteria in a table format for performance metrics like "leak detection accuracy." Instead, it focuses on demonstrating that the device's leak detection capabilities are "similar" to a predicate device. The performance is assessed through verification testing rather than a clinical trial with statistical endpoints.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document does not specify the sample size of endoscopes used for the performance testing or the provenance of the data. It mentions "The Device was successfully tested for all functions, performance, and safety."

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)

This is not applicable as the device is an automated leakage tester. The "ground truth" for a leak would be an objective physical event detected by the device, not a subjective interpretation by a human expert.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable.

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, as this is not an AI/ML device for human interpretation augmentation.

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

The device is inherently a standalone automated tester. Its performance was evaluated on its own ("The Device was successfully tested for all functions, performance, and safety"). The comparison was against another device's capability, not against human performance.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

The "ground truth" for leakage testing would likely be based on objective physical measurements of air pressure changes within the endoscope, or visual observation of bubbles during a wet test, indicating a leak. The document does not explicitly detail the method for establishing "ground truth" for leaks during testing, but it would be an objective, physical phenomenon directly measured by the device and a comparator.

8. The sample size for the training set

Not applicable, as this is not an AI/ML device that requires a training set.

9. How the ground truth for the training set was established

Not applicable.

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