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This video system center is intended to be used with OLYMPUS video converter, camera heads, endoscopes, monitors, EndoTherapy accessories, and other ancillary equipment for endoscopic diagnosis, treatment, and video observation.

The VIDEO SYSTEM CENTER OLYMPUS CV-170 is intended for endoscopic diagnosis, treatment and video observation. The CV-170 is a device which integrates the video processor and the light source. Thus, the CV-170 itself could emit light, capture and process the endoscopic image, and transmit signal to display it on the monitor. The main function of the CV-170 is NBI observation, LED lamp examination, Noise reduction function, Pre-freeze feature, and Color correction.

This document is a 510(k) Summary for the Olympus CV-170 Video System Center. It describes the device, its intended use, and compares it to predicate devices. However, this document does not contain the results of a primary clinical study with acceptance criteria, reported device performance, or details about ground truth establishment.

Instead, this document, typical for a 510(k) submission, focuses on demonstrating substantial equivalence to a legally marketed predicate device (OLYMPUS CV-190 VIDEO SYSTEM CENTER and OLYMPUS CLV-190 XENON LIGHT SOURCE, K112680). The "Summary of non-clinical testing" section mentions:

• "Risk analysis was carried out in accordance with established in-house acceptance criteria based on ISO 14971:2007. The design verifications tests and their acceptance criteria were identified and performed as a result of this risk analysis assessment."
• "The software validation activities were performed in accordance with the FDA Guidance, 'Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices.'"

This indicates that internal design verification and software validation tests were performed against in-house acceptance criteria to ensure the device's safety and effectiveness compared to the predicate, but the specific details of these acceptance criteria and performance results are not included in this summary.

Therefore, I cannot provide the requested information in the format you specified because it is not present in the provided text. The document states that non-clinical testing was performed, focusing on risk analysis, design verification, and software validation. It does not describe a study involving human readers or the evaluation of an AI algorithm against clinical ground truth.

Specifically, the questions that cannot be answered from the provided text are:

1. A table of acceptance criteria and the reported device performance: The document states that internal acceptance criteria were used for risk analysis and design verification, but it does not list them or the specific performance results in a table.
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): No test set sample size or data provenance is mentioned as there's no clinical performance study reported.
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): No ground truth establishment is described for a test set.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: No adjudication method is 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 or AI assistance is mentioned. This device is a video system center, not an AI-driven diagnostic tool.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: No standalone algorithm performance is mentioned.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): No ground truth type is mentioned.
8. The sample size for the training set: No training set or sample size is mentioned.
9. How the ground truth for the training set was established: No ground truth establishment for a training set is mentioned.

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