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The EG-2940, Video Gastroscope, is intended to provide visualization (via a video monitor) of, and therapeutic access to, the Upper Gastrointestinal Tract. The Upper Gastrointestinal Tract includes, but is not restricted to, the organs; tissues; and subsystems: Esophagus, Stomach, Duodenum, and Small Bowel. The instrument is introduced per orally when indications consistent with the requirement for the procedure are observed in Adult and Pediatric patient populations.

The EG-2940, Video Gastroscope, must be used with a Video Processor (a software controlled device). The endoscope has a flexible insertion tube. a control body, and umbilicus. The umbilicus provides connection to the video processor. The control body includes controls for up/ down/ left/ right angulation, air/water delivery, suction, and an accessory inlet port. The device contains light carrying bundles, to illuminate the body cavity, and a charge couple device (CCD) to collect image data. The instrument contains a working channel through which biopsy devices, or other devices, may be introduced (the instrument is supplied with two biopsy forceps). The Video Processor contains a 300 watt xenon lamp which provides white light that is filtered, via a Red, Green, Blue color wheel, and is focused at the connected video endoscope lightguide prong. The endoscope light carrying bundles present the color strobes to the body cavity and the CCD collects image data for each strobe of color. The video processor stores the CCD information until all three color strobes are completed and a full color frame is compiled. Image data and other screen display information are formatted and presented to the video outputs of the video processor for display.

Here's the analysis of the provided text regarding acceptance criteria and study details:

Unfortunately, the provided text does not contain any information about acceptance criteria or a study designed to prove the device meets those criteria.

The document is a 510(k) summary for the Pentax EG-2940 Video Gastroscope. A 510(k) summary primarily focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than presenting a performance study with detailed acceptance criteria.

The key statement confirming this is:
"The submission for substantial equivalence was not based on an assessment of clinical performance data."

Therefore, I cannot populate the requested table or answer the specific questions about sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, or ground truth for a performance study, as this information is explicitly stated as not being part of this submission's basis.

Here's how I would present the information based on your request, highlighting the absence of the requested data:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

No test set or performance study data is described in this 510(k) summary. The submission for substantial equivalence was not based on an assessment of clinical performance data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. No performance study or test set described.

4. Adjudication method for the test set

Not applicable. No performance study or test set described.

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

No MRMC study was done. The device described is a video gastroscope, not an AI-powered diagnostic tool. The submission was not based on clinical performance data.

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

Not applicable. The device described is a physical medical instrument (gastroscope) requiring human operation, not a standalone algorithm. The submission was not based on clinical performance data.

7. The type of ground truth used

Not applicable. No performance study data is described.

8. The sample size for the training set

Not applicable. No machine learning model or training set is described.

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

Not applicable. No machine learning model or training set is described.

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