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The water container MAJ-901 has been designed to be used with endoscopes to allow gas insufflation and water feeding from the endoscope.

Do not use this instrument for any purpose other than its intended use.

The water container MAJ-902 has been designed to be used with endoscopes to allow gas insufflation and water feeding from the endoscope.

Do not use this instrument for any purpose other than its intended use.

The MAJ-901 and MAJ-902 have been designed to be used with Olympus light sources and endoscopes to allow gas insufflation and water feeding from the endoscope.
MAJ-901 and MAJ-902 are both comprised of a Lid, Container, O-ring, and Container Protector. Other than Lid, the devices are the same.
MAJ-902 also includes a "gas tube connector" in the lid for carbon dioxide gas delivery. Additionally, MAJ-902 includes a Caution tag that the MAJ-901 does not.

This is a 510(k) summary for a medical device (Water Container MAJ-901 and MAJ-902), which typically focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed study proving the device meets specific acceptance criteria in the way a clinical trial for a new therapeutic might.

Based on the provided document, here's what can be extracted regarding acceptance criteria and the "study" (non-clinical testing) that proves the device meets them:

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

The document mentions that all subject devices met design specifications and performed as intended in the non-clinical tests. However, it does not provide a table with specific quantitative acceptance criteria or the numerical performance results of these tests. Instead, it offers a high-level summary of the types of tests conducted:

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 provide details on the sample sizes used for each of the non-clinical tests (e.g., how many water containers were tested for flow rate, durability, etc.). Similarly, it does not specify the country of origin of the data or whether the tests were retrospective or prospective. It is implied these were prospective bench tests conducted by the manufacturer.

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 to this type of 510(k) submission. The "ground truth" for non-clinical bench testing often refers to established engineering standards or internal design specifications, verified through objective measurements, rather than expert consensus on medical images or clinical outcomes.

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

Not applicable. Adjudication methods like 2+1 or 3+1 are typically used in clinical studies or studies involving human readers/interpreters where there's a need to resolve discrepancies in expert opinions. This document describes bench testing where performance against technical specifications is directly measured.

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 is a medical device for water and gas feeding in endoscopes, not an AI-powered diagnostic or assistive tool for human readers. Therefore, an MRMC study is not relevant.

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

Not applicable. This device is a water container, not an algorithm.

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

For the non-clinical tests mentioned, the "ground truth" would be the engineering design specifications and recognized industry standards for flow rate, durability, biocompatibility, and reprocessing effectiveness. Performance was measured against these predefined technical parameters.

8. The sample size for the training set

Not applicable. This device is not an AI algorithm that requires a training set.

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

Not applicable, as there is no training set for this device.

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