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The Stryker Precision HD Camera System is indicated for use in general laparoscopy, nasopharyngoscopy, ear endoscopy, sinuscopy, and plastic surgery wherever a laparoscope/endoscope/arthroscope is indicated for use.

A few examples of the more common endoscopic surgeries are listed below.

• · laparoscopic cholecystectomy
• · laparoscopic hernia repair
• · laparoscopic appendectomy
• laparoscopic pelvic lymph node dissection
• · laparoscopically assisted hysterectomy
• · laparoscopic and thorascopic anterior spinal fusion
• anterior cruciate ligament reconstruction
• · knee arthroscopy
• · shoulder arthroscopy
• small joint arthroscopy
• · decompression fixation
• · wedge resection
• · lung biopsy
• · pleural biopsy
• dorsal sympathectomy
• · pleurodesis
• · internal mammary artery dissection for coronary artery bypass
• coronary artery bypass grafting where endoscopic visualization is indicated
• · examination of the evacuated cardiac chamber during performance of valve replacement

The users of the camera are general surgeons, gynecologists, cardiac surgeons, plastic surgeons, orthopedic surgeons, ENT surgeons and urologists.

The Stryker Precision HD Camera Systems (hereafter referred to as "proposed device") is an endoscopic camera system that is used together with an endoscope to view endoscopic surgical sites on video monitors. The system consists of a Camera Control Unit (CCU), a Coupler, and a Camera Head with an integral cable that connects to the CCU. The optical image is transferred from the surgical site to the camera head by a variety of rigid and flexible scopes that are attached to the camera head using an optical coupler or by direct connection. Sterilization of the Coupler and Camera Head is required before use. The Coupler and Camera Head can be sterilized using steam sterilization.

The provided text describes a 510(k) submission for the Stryker Precision HD Camera System, which is a medical device for endoscopic visualization in various surgical procedures. Unfortunately, the document does not contain an "acceptance criteria" table with specific device performance metrics in numerical form.

Instead, the document focuses on demonstrating substantial equivalence to a predicate device (Stryker 1488 HD Video Camera with Infrared Compatibility) by highlighting similar technological characteristics and adherence to various safety and performance standards. The "Performance Data" section describes the types of tests conducted but does not provide quantitative results or specific acceptance criteria for those tests.

Therefore, many of the requested details, such as specific performance metrics and their acceptance criteria, sample sizes for test and training sets, details about ground truth establishment, or information about MRMC studies, are not present in this 510(k) summary.

However, I can extract the following information that is available in the text:

1. Table of Acceptance Criteria and Reported Device Performance:

As mentioned, a specific table with numerical acceptance criteria and reported device performance is not included in this document. The document states that "Bench performance testing was conducted to ensure that the device functioned as intended and met design specifications and acceptance criteria." However, it does not detail what those specific criteria or the measured performance values were. It primarily focuses on compliance with standards.

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

• Sample Size for Test Set: Not specified. The document mentions "Bench performance testing" and compliance with standards but does not provide a specific sample size for a test set of data. This submission is for a camera system, not for an AI algorithm or a diagnostic device that processes specific patient data for a diagnostic outcome.
• Data Provenance: Not applicable in the context of an AI device's test set. The performance testing described relates to the camera's hardware and software functionality and compliance with electrical, EMC, and sterilization standards.

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

• Not applicable. This document describes a camera system for visualization, not a diagnostic device that requires expert-established ground truth on medical images or data for performance evaluation. The "truth" in this context would be the camera's ability to accurately capture and display images, which is assessed through technical performance testing, not expert interpretation of medical cases.

4. Adjudication Method for the Test Set:

• Not applicable. See point 3. Adjudication methods are typically used for diagnostic studies involving human interpretation or AI outputs, not for the functional testing of a camera system.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

• No. An MRMC study was not done or described. This type of study is relevant for evaluating the impact of an AI system on human reader performance, which is not the scope of this device's submission as presented.

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

• Not applicable. This device is a camera system, and while it contains software, its primary function is to provide visual input to a human surgeon. It is not an "algorithm only" device in the sense of an AI diagnostic tool. Its performance is intrinsically linked to its use in a human-in-the-loop context during surgery.

7. The Type of Ground Truth Used:

• Technical Specifications/Standards. For this type of device (a camera system), the "ground truth" used for testing would be compliance with established engineering design specifications, performance standards (e.g., image resolution, color accuracy, electrical safety, electromagnetic compatibility), and successful sterilization validation. It does not involve medical ground truth like pathology or outcomes data in the way an AI diagnostic tool would.

8. The Sample Size for the Training Set:

• Not applicable / Not specified. This document does not describe an AI system that undergoes training on a dataset of medical cases. The software mentioned is for controlling the camera system (e.g., user interface, surgical mode selections), not for an AI model trained on data for diagnostic or assistive tasks.

9. How the Ground Truth for the Training Set was Established:

• Not applicable / Not described. See point 8. There is no mention of a training set or corresponding ground truth establishment for an AI algorithm.

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