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The LENS 4K Camera Control Unit and LENS 4K Camera Head are used in diagnostic and operative procedures for arthroscopic and endoscopic procedures to provide illumination, visualization and capture of surgical sites within articular cavities, body cavites, hollow organs and canals. Additionally, the LENS 4K Camera Control Unit and LENS 4K Camera Head are indicated for use in endoscopic surgical procedures in the thoracic cavity when used with an appropriately indicated thoracoscope.

The Smith & Nephew LENS 4K System ( 4K Camera Control Unit (CCU) and LENS 4K Camera Head), is an integrated camera/ LED light source/ image management device that provides visualization, illumination and image/video capture combined in a single console. The LENS 4K CCU works in conjunction with a camera head, appropriate light guides, couplers and endoscopes. The LENS 4K CCU will be offered in two versions, one with Wi-Fi and one without Wi-Fi. The non-Wi-Fi version does not have the ability to communicate to a tablet but captures the data to an attached USB storage device or to the Smith & Nephew 660HD-E Image Management System, which is a Class 1 device per LMB for display and sharing for display and sharing. Additionally, the LENS 4K System will also work with an optional Tablet Application, which was cleared in K190367. The Tablet application, when downloaded to a mobile device, acts as a tool to remotely capture video recordings and image files wirelessly from the LENS 4K System. The application provides the ability to view and annotate by voice or graphics or to text. Sharing of patient data from the Tablet is provided via the ability to archive to a USB storage device on the CCU, email, printing, or export to the local tablet camera roll.

This FDA 510(k) summary for the Smith & Nephew LENS 4K System (K191177) describes a device that provides illumination, visualization, and image/video capture for arthroscopic and endoscopic procedures. The information provided heavily focuses on demonstrating substantial equivalence to a predicate device (Smith & Nephew LENS Integrated System, K153606), rather than presenting a detailed study proving the LENS 4K System meets specific performance acceptance criteria found in a typical clinical trial.

However, based on the provided text, we can extract details related to performance testing and acceptance criteria to the extent possible.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative "acceptance criteria" against which "device performance" is reported in a numerical table format, as would be common for a medical imaging AI product. Instead, it lists categories of testing performed to demonstrate that the device "met the established performance specifications." Therefore, the table below reflects the types of testing done and the general conclusion drawn, rather than specific numerical criteria and results.

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

This information is not provided in the document. The filing focuses on demonstrating substantial equivalence based on engineering and performance testing rather than clinical study data. There is no mention of a "test set" in the context of patient data or images.

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

This information is not provided in the document. As there is no mention of a test set involving patient data/images for which ground truth would be established by experts, this section is not applicable.

4. Adjudication Method for the Test Set

This information is not provided in the document. Not applicable given the absence of a clinical test set.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

A multi-reader multi-case (MRMC) comparative effectiveness study was not mentioned or performed based on the provided document. The device is a camera system, not an AI-assisted diagnostic tool designed to improve human reader performance.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

A standalone performance study in the context of an algorithm's diagnostic accuracy was not performed or mentioned. This device is a camera system for visualization during surgery, not an AI algorithm performing a diagnostic task. The "performance data" mentioned (video output, temperature, software V&V, etc.) refers to the engineering and functional performance of the hardware and associated software.

7. Type of Ground Truth Used

The concept of "ground truth" as typically applied to diagnostic AI algorithms (e.g., pathology, expert consensus) is not applicable here. The performance testing described (Video Output, Camera Head Surface Temperature, Software V&V, Emissions, Electrical Safety) would rely on objective engineering measurements and verification against design specifications and relevant standards, rather than clinical ground truth derived from patient outcomes or expert interpretations.

8. Sample Size for the Training Set

This information is not provided in the document. The device is a hardware system with associated software, not a machine learning model that requires a training set in the AI sense.

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

This information is not provided in the document. Not applicable as there is no mention of a training set for an AI model.

Summary of Study Type:

The provided document describes a 510(k) premarket notification for a medical device (a 4K camera system for endoscopy). The 'study' referred to is a series of engineering and functional performance tests conducted on the device itself to ensure it meets established design specifications and relevant regulatory standards (e.g., electrical safety, emissions). The primary goal of the submission is to demonstrate substantial equivalence to an existing predicate device (Smith & Nephew LENS Integrated System, K153606) based on similar intended use, fundamental scientific technology, basic design, and comparable performance. It is not a clinical study evaluating diagnostic accuracy or physician performance, nor is it related to AI/ML software performance studies that would typically involve test sets, ground truth, and expert readers.

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