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Camera System: The Camera System is indicated for use in general laparoscopy, ear endoscopy, ear endoscopy, sinuscopy, and plastic surgery whenever a laparoscope/ endoscope/ arthroscope is indicated for use. A few examples of the more common endoscope surgeries are Laparoscopic cholecystectomy, Laparoscopic hernia repair, Laparoscopic appendectomy, Laparoscopic pelvic lymph node detection, Laparoscopically assisted hysterectomy, Laparoscopic and thorascopic anterior spinal fusion, Anterior cruciate ligament reconstruction, Knee arthroscopy, Decompression fixation, Wedge resection, Lung biopsy, Pleural biopsy, Dorsal sympathectomy, Pleurodesis, Internal mammary artery dissection for coronary artery bypass grafting where endoscopic visualization is indicated and Examination of the evacuated cardiac chamber during performance of valve replacement. The users of the Camera System are general surgeons, gynecologists, cardiac surgeons, plastic surgeons, orthopedic surgeons, ENT surgeons and urologists.

NIR FI Light Source: The NIR FI Light Source and NIR FI Light Guide are indicated for use to provide real-time endoscopic visible and nearinfrared fluorescence imaging. The NIR FI Light Source and NIR FI Light Guide 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 duct, common bile duct and common hepatic duct), using near-infrared imaging.

Fluorescence imaging of biliary ducts with the NIR FI Light Source and NIR FI Light Guide is intended for use with standard-of-care white light and, when indicated, intraoperative cholangiography. The devices are not intended for standalone use for biliary duct visualization.

The individual components of the subject device, SCHOELL Y's NIR FI System, form a system to provide real-time endoscopic visible imaging (wight light imaging, WLI) and near-infrared (NIR) illumination and imaging (fluorescence imaging, FI) using indocyanine green (ICG):

• Camera System suitable for processing and recordings visible light images as well as NIR images. The Camera System consists of a Camera Control Unit (CCU) and a Camera Head for connection to a fiberoptic scope;
• . Light Source and Light Guide for use with a fiberoptic scope for emitting light within the visible spectrum as well as in the NIR spectrum to cause fluorescence;
• . Fiberoptic Laparoscope suitable for visible light and NIR light illumination and imaging;

The imaging agent (ICG) is not provided by SCHOELLY as part of the subject system.

The submitted information does not contain a study that proves the device meets the acceptance criteria in the format requested. The document primarily focuses on establishing substantial equivalence to a predicate device for regulatory clearance. It describes general performance testing conducted, but not in the context of specific acceptance criteria and detailed study results as typically found in clinical trials or dedicated performance studies for AI/ML devices.

However, based on the provided text, I can extract information related to the device and the types of testing performed to support its regulatory clearance.

Here's an attempt to structure the available information, noting where specific details (like acceptance criteria, sample sizes, ground truth establishment, or expert involvement for performance scores) are not explicitly present in the provided document:

Device Name: Near-Infrared (NIR) Fluorescence Imaging (FI) System: Camera System (Camera Control Unit, Camera Head to be coupled to a fiberoptic scope), NIR FI Light Source

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria for device performance (e.g., specific sensitivity, specificity, accuracy targets). Instead, it relies on demonstrating compliance with recognized standards and substantial equivalence to a predicate device.

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify a "test set" in the context of evaluating a dataset for AI performance. The performance data mentioned refers to engineering and quality system validation tests. No information is provided regarding the origin (country, retrospective/prospective) of specific data sets used for validating imaging performance beyond general statements about "non-clinical performance test data."

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

Not applicable based on the provided text. The document does not describe a study involving expert readers establishing ground truth for a test set to assess AI performance. The focus is on the device's technical specifications and safety/effectiveness in a comparative context to a predicate device.

4. Adjudication Method for the Test Set

Not applicable. As no human expert evaluation of a test set for AI performance is described, no adjudication method is mentioned.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size

No MRMC study is mentioned. The document primarily focuses on demonstrating substantial equivalence to a predicate device through technical comparisons and compliance with standards, rather than a study comparing human readers with and without AI assistance.

6. If a Standalone (Algorithm Only) Performance Study Was Done

The device described is a medical imaging system (hardware and integrated functionality for visible and NIR fluorescence imaging), not a standalone AI algorithm. Therefore, a standalone algorithm performance study as typically understood for AI/ML software is not applicable here. The "Non-Clinical Performance Testing" refers to the system as a whole.

7. The Type of Ground Truth Used

For the "Non-Clinical Performance Testing," the "ground truth" implicitly refers to the expected performance characteristics based on an existing predicate device and the design input requirements for endoscopic white light and near-infrared fluorescence imaging. The document does not specify an external "ground truth" like pathology, expert consensus on images, or outcomes data.

8. The Sample Size for the Training Set

Not applicable. The document does not describe the development or training of an AI algorithm with a training set. The device is an imaging system, not a machine learning model.

9. How the Ground Truth for the Training Set Was Established

Not applicable. As there is no mention of a training set or AI algorithm training, the establishment of ground truth for such a set is not discussed.

Summary of Non-Inclusion:

The provided document is a 510(k) summary for a medical device (an imaging system) seeking clearance based on substantial equivalence. It is not a report on a clinical or performance study evaluating an AI/ML algorithm against specific performance metrics with independent test sets and expert ground truth. Therefore, many of the requested details, particularly those related to AI/ML study design (sample sizes for test/training sets, expert qualifications, ground truth establishment, MRMC studies), are not present in the given text. The "Performance Data" section details compliance with recognized safety, software, and reprocessing standards, and general non-clinical performance demonstrations for substantial equivalence.

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