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Upon intravenous administration of ICG(Indocyanine green for injection) consistent with its approved label, the OPTOVISION provides real-time endoscopic visible and near-infrared fluorescence imaging. The OPTOVISION enable surgeons to perform minimally invasive surgery using standard endoscope visual light as well as visual assessment of vessels, blood flow and related tissue perfusion, and at least one of the major extra-hepatic bile ducts (cystic duct, common bile duct and common hepatic duct), using near-infrared imaging. Fluorescence imaging of biliary ducts with the OPTOVISION is intended for use with standard-of-care white light and, when indicated, intraoperative cholangioaraphy. The device is not intended for standalone use for biliary duct visualization.

Upon interstitial administration and use of ICG consistent with its approved label, the OPTOVISION is used to perform intraoperative fluorescence imaging and visualization of the lymphatic system, including lymphatic vessels and lymph nodes.

OPTOVISION is an endoscopic light source that enables real-time endoscopic visible and nearinfrared fluorescence imaging minimally invasive surgical procedures. Near-infrared illumination is used for fluorescence imaging using indocyanine green (ICG). This device is largely composed of a power circuit, a control circuit, and a lighting lamp (light source). A power circuit provides power to the lighting lamp and cuts off the power in an emergency, a control circuit controls the light output (intensity)/other settings of light rays, and a light source unit irradiates light directly to the Light Guide Cable (K111342) for observation of the affected area. With the configuration above, the power input from the initial power source is transmitted to the light source unit and the main body, and rays of white light and near-infrared light are directly irradiated through the Light Guide Cable to the affected area for observation. This device is designed for use in the following applications: standard endoscopic visible-light imaging and near-infrared fluorescence imaging.

The provided FDA 510(k) summary (K243591) for the "OPTOVISION Endoscopic Light Source Unit" describes the device and its comparison to a predicate device, but it does not present an acceptance criteria table or report specific device performance metrics in the way typically seen for AI/ML device studies (e.g., sensitivity, specificity, AUC).

Instead, this submission is for an endoscopic light source, and its "performance" is demonstrated through compliance with established electrical, electromagnetic, usability, photobiological safety, and software validation standards, as well as a general "Performance - Bench" test. This type of device, an endoscopic light source, does not inherently involve an AI algorithm with output metrics like those you've requested. Thus, many of the specific questions about sample sizes, expert ground truth, adjudication, MRMC studies, and standalone performance are not applicable to the information provided.

However, I can extract and present the available information in a structured format as best as possible, noting where the requested details are not present in the document.

Explanation regarding the absence of typical AI/ML study information:

The "OPTOVISION Endoscopic Light Source Unit" is classified as an "Endoscope And Accessories" (21 CFR 876.1500) with product code OWN, which refers to "Confocal Optical Imaging". This device is a light source that enables real-time endoscopic visible and near-infrared fluorescence imaging using Indocyanine green (ICG). While it uses near-infrared light for fluorescence imaging, the document does not describe it as an AI/ML device that performs diagnosis or prediction, hence the absence of metrics like sensitivity, specificity, and the related study design details. Its "performance" is primarily assessed against safety and engineering standards.

Acceptance Criteria and Device Performance (Based on available information)

Since this is an endoscopic light source, the "acceptance criteria" are compliance with relevant safety and performance standards for medical electrical equipment and usability. The "reported device performance" is the successful passing of these tests.

Study Details (Based on available information)

1. Sample size used for the test set and the data provenance:

   • Test Set Sample Size: Not applicable. The "performance data" describes compliance with engineering and safety standards, not a clinical study on a population of patients or data sets in the context of AI/ML.
   • Data Provenance: Not applicable. The "tests" are engineering and safety evaluations of the device itself.

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

   • Not applicable. This device is a light source, not an AI/ML algorithm requiring expert-established ground truth for its output. The "ground truth" for these tests would be the specifications and requirements of the standards themselves.

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

   • Not applicable. This type of adjudication is for disagreements in expert readings/labeling, which does not apply to compliance testing against engineering standards.

4. 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, as this device is a light source and not an AI assistant intended to improve human reader performance.

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

   • Not applicable. This is not an AI algorithm performing a standalone task. Its function is to provide illumination for human visualization.

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

   • Not applicable. The "ground truth" for the performance data are the requirements and specifications defined by the referenced international and national standards (e.g., IEC, ES standards) for medical device safety, EMC, usability, photobiological safety, and software.

7. The sample size for the training set:

   • Not applicable. The device is not an AI/ML model that requires a training set.

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

   • Not applicable. As above, there is no AI/ML training set.

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