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The ConMed True HD 3MOS Camera System is intended for use in a variety of endoscopic surgical procedures including but not limited to orthopedic, laparoscopic, urologic, sinuscopic, plastic and as an accessory for microscopic surgery.

The ConMed True HD 3MOS Camera System consists of a camera control unit (CCU) and a camera head that is used in conjunction with endoscopes, light source, light guide cables, monitors and other ancillary equipment to allow for high definition visualization during minimally invasive surgical procedures.

The provided text is a 510(k) summary for the ConMed True HD 3MOS Camera System. This document focuses on demonstrating substantial equivalence to a predicate device and does not contain detailed information about specific acceptance criteria or the study design with the granularity requested. Medical device 510(k) submissions typically summarize the testing performed, but do not often provide the full study details, especially for performance characteristics that are not directly tied to a clinical outcome or a specific diagnostic accuracy metric.

However, based on the limited information available in the provided text, here's what can be extracted and what cannot:

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

The document broadly states: "The tests results demonstrated the safety and effectiveness of the devices in accordance with design specifications and applicable standards." and "Performance testing were conducted to demonstrate that the True HD 3MOS Camera System performs according to specifications and functions as intended."

However, specific quantitative acceptance criteria (e.g., minimum resolution, signal-to-noise ratio, color accuracy range) and their corresponding measured performance results are NOT provided in this summary.

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 mentions "Performance testing" and "System Testing" (Verification and Validation including Functional testing, Video Image, Software). It also lists various standards evaluations (Electromagnetic compatibility, Electrical Safety, Reprocessing, Risk analysis, Software validation).

However, specific sample sizes for these tests (e.g., number of camera units tested, number of video sequences analyzed) are NOT provided. Details regarding data provenance (e.g., country of origin, retrospective or prospective nature of data collection) are also NOT provided. These are typically engineering performance tests, not clinical studies.

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 product is an endoscopic camera system for visualization during surgery. The primary performance characteristic is image quality for surgical viewing. The document does not mention any use of experts to establish 'ground truth' for image quality or clinical outcomes in the context of a diagnostic or comparative study with human readers. The assessment of image quality in such devices is typically done objectively against technical specifications and subjectively by engineers or clinical specialists without a formal 'ground truth' establishment process as seen in diagnostic AI.

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

Since no experts were mentioned for establishing ground truth or evaluating diagnostic performance, there is no adjudication method described or 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

No MRMC comparative effectiveness study was done or mentioned. This device is a camera system, not an AI software intended to assist human readers in interpretation. The goal is to provide high-definition visuals to the surgeon.

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

This refers to an "algorithm only" performance. The ConMed True HD 3MOS Camera System is a hardware device (camera system) used for visualization. While it has software components, its primary function is not an "algorithm only" diagnostic performance that would typically be evaluated in this manner. The performance testing focuses on the camera's ability to produce images according to specifications.

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

For a camera system, "ground truth" would typically relate to objective image quality metrics (resolution, color accuracy, brightness, latency) compared against engineering specifications, or the ability to correctly visualize anatomical structures. The document states "The tests results demonstrated the safety and effectiveness of the devices in accordance with design specifications and applicable standards." This implies the "ground truth" for the device's performance is adherence to established technical specifications and standards (e.g., video resolution of 1920 x 1080 pixels). It does not mention ground truth based on expert consensus, pathology, or outcomes data in a clinical sense.

8. The sample size for the training set

This device does not appear to involve machine learning in a way that requires a 'training set' for a diagnostic algorithm. The software leveraged "key components from a camera base component procured from Panasonic (SOUP)." This implies it's an off-the-shelf software component, not a custom-trained AI model requiring a specific training dataset as understood in AI/ML performance evaluation. Therefore, there is no mention of a training set sample size.

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

As there is no training set for a machine learning model, there is no ground truth establishment method described for a training set. The "ground truth" for this camera system would be based on its compliance with engineering and safety standards, as well as its ability to capture and display high-definition video during surgical procedures.

In summary: The provided 510(k) summary is for a hardware medical device (camera system) and focuses on demonstrating substantial equivalence through technical performance verification and validation, adherence to standards, and risk analysis. It does not contain the detailed information typically found in submissions for AI/ML-based diagnostic devices, which would involve concepts like training/test sets, ground truth establishment by experts, and comparative effectiveness studies with human readers.

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