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The PeerScope System is intended for diagnostic visualization of the digestive tract. The system also provides access for therapeutic interventions using standard endoscopy tools. The PeerScope System is indicated for use within the upper digestive tract (including the esophagus, stomach, and duodenum).

The PeerScope System Model HG is a G1 platform for diagnostic visualization and therapeutic intervention of the digestive tract for use in healthcare facility/hospital. The system consists of the Main Control Unit (MCU) Videoprocessor that provides the device controls, user interfaces, image processing, pneumatic controls and interfaces with various external accessories, and of the PeerScope GS flexible video Gastroscope labeled for repeatable clinical usage within the upper digestive tract. The operation principles of the PeerScope System are similar to those of other legally marketed standard Gastroscopic systems. The system also provides the physicians with two viewing capabilities: Standard 160° front field of view, and 210° wide field of view.

The provided document describes the PeerScope System (Model HG), a gastroscope used for diagnostic visualization and therapeutic interventions in the upper digestive tract. The submission to the FDA focuses on establishing substantial equivalence to a predicate device, the EVIS EXERA II 180 System (K100584), rather than a standalone clinical study demonstrating specific performance metrics against pre-defined acceptance criteria for a new AI-powered diagnostic device.

Therefore, many of the requested elements for an AI device study are not applicable to this submission, as it concerns a medical device with similar technological characteristics to existing devices, seeking clearance under the 510(k) pathway.

However, I can extract information related to the device's performance, testing, and basis for demonstrating safety and effectiveness as presented in the document.

Summary of Acceptance Criteria and Device Performance (as inferable from the 510(k) submission):

The submission does not present a table of explicit acceptance criteria for a novel AI algorithm's performance. Instead, the "acceptance criteria" are implied by demonstrating that the PeerScope System Model HG performs equivalently to, and is as safe and effective as, its legally marketed predicate devices. The performance data presented focuses on design verification, software validation, reprocessing validation, and usability, all of which met their respective internal and regulatory acceptance criteria.

Detailed Responses to Specific Questions:

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

   • Please refer to the table above. The acceptance criteria are largely implied by demonstrating substantial equivalence to the predicate device and compliance with applicable standards.

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

   • Usability Study: Five experienced GI physicians participated in the usability testing.
   • Data Provenance: The usability testing was conducted in a U.S. medical center. The nature of this testing (observational use in a clinical environment) makes it prospective rather than retrospective.
   • For other tests (Risk Analysis, Design Verification, Software Validation, Reprocessing Validation, Bench Data), the sample sizes are not explicitly stated, as these typically refer to engineering verification and validation activities rather than clinical study cohorts. The "device safety and performance were verified by EndoChoice Innovation Center Ltd. and accredited third party laboratories" (Israel and likely other locations for third-party labs).

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):

   • For the usability study, five experienced GI physicians were used. Their specific years of experience are not detailed, but "experienced" implies sufficient expertise for evaluating a gastroscope.
   • For other technical verification tests, "ground truth" is established by engineering specifications, regulatory standards, and validated test methods, rather than expert consensus on diagnostic interpretations.

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

   • The document does not describe an adjudication method for the usability study. It states that device usability was "carried out by means of testing within a clinical environment ... by five experienced GI physicians." It implies a consensus or individual assessment by these physicians that the device meets its specifications for usability. Given the nature of a 510(k) for an endoscope, a formal adjudication process for diagnostic accuracy (like in an AI study) is not typically required.

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, an MRMC comparative effectiveness study was not done. This submission is for a medical device (gastroscope) and not an AI-powered diagnostic tool. The focus is on demonstrating substantial equivalence to a predicate device, not on improving human reader performance with AI assistance.

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

   • Not applicable. This submission is for an endoscope system, not an AI algorithm. There is no standalone algorithm performance to report.

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

   • For the usability study, the "ground truth" was the device's adherence to its specifications and satisfactory performance in a clinical environment, as assessed by experienced GI physicians.
   • For bench testing and design verification, the "ground truth" comprises engineering specifications, performance standards (e.g., scope angulation, field of view, depth of field), and regulatory compliance requirements.

8. The sample size for the training set:

   • Not applicable. As this is not an AI device, there is no training set mentioned or required.

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

   • Not applicable. As there is no training set, there is no ground truth establishment for it.

In conclusion, this 510(k) submission for the PeerScope System (Model HG) demonstrates its safety and effectiveness primarily through bench testing, adherence to recognized standards, and a small-scale usability study, all aimed at proving substantial equivalence to a previously cleared predicate device. It does not involve the type of clinical performance study with acceptance criteria and ground truth validation that would be typical for a novel AI-driven diagnostic device.

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