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The Smith & Nephew Control Digital Operating Room System is indicated for use with compatible endoscopic and surgical equipment for centralized control of these devices and display of device status. The control system provides centralized touch panel and voice control of medical devices for use by the surgeon and O.R. staff.

The proposed Smith & Nephew Control Digital Operating Room System is a computer based system that enables centralized status display and control of multiple medical and non-medical devices through touch panel activation or through an optional voice recognition feature. The primary system consists of a Medical Device Controller, Medical Device Hub, Primary Touch Panel Interface (TPI-P), Surgical Touch Panel Interface (TPI-S), Ethernet Router and AVB network hub and AVB Power Supply. Optional components include: speech recognition engine, a wireless microphone for speech recognition, a wireless touch screen interface that can be bagged and placed in the sterile field, a display controller which allows device settings and conditions to be displayed on the surgeon's monitor.

Here's an analysis of the provided text regarding the Smith & Nephew Control Digital Operating Room System, addressing the requested information:

1. Table of Acceptance Criteria & Reported Device Performance

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

This information is not provided in the document. The submission references "performance testing" but does not detail the methodology, sample sizes, or data provenance (e.g., country of origin, retrospective/prospective nature) for any specific tests performed beyond adherence to general safety and EMC standards.

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

This information is not provided in the document. The submission focuses on substantial equivalence to predicate devices and adherence to regulatory standards, not on clinical-performance-based ground truth established by experts.

4. Adjudication Method for the Test Set

This information is not provided in the document. Given the nature of the device (a control system for existing medical devices) and the focus on substantial equivalence to predicate devices and adherence to regulatory standards, a formal adjudication method for a "test set" in the sense of clinical image or data interpretation is unlikely to have been a primary component of this submission.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

This information is not provided and is not applicable to this device. The Smith & Nephew Control Digital Operating Room System is a control system for medical devices, not an AI-powered diagnostic or interpretive tool that would involve "human readers" or "AI assistance" in the typical MRMC study context.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Study Was Done

This information is not provided and is not entirely applicable in the context of an algorithm's interpretive performance. The device itself is a standalone system for controlling other medical devices. Its "performance" would be assessed in terms of its ability to correctly send commands and display status, which is inherent in its function. No separate "algorithm-only" study examining diagnostic accuracy (as typically implied by this question) would be relevant here.

7. The Type of Ground Truth Used

The "ground truth" used for this submission appears to be:

• Adherence to recognized voluntary standards: IEC 60601-1, IEC 60601-1-1, IEC 60601-1-2, UL 60601-1, CAN/CSA 22.2 No. 601.1 regarding safety and EMC.
• Functional equivalence to predicate devices: The ability of the system to provide "centralized and remote control of medical devices in the O.R." in a manner deemed equivalent to existing, legally marketed systems (Olympus EndoALPHA, HERMES, Sidne™). This implies that the accepted functionality of the predicate devices serves as a benchmark for "ground truth" performance.

No pathology, expert consensus on diagnostic outcomes, or patient outcomes data is mentioned as ground truth.

8. The Sample Size for the Training Set

This information is not provided in the document. As this is a control system for operational devices rather than a machine learning algorithm that is "trained" on data to perform a specific task (e.g., image classification), the concept of a "training set" in the context of AI/ML is not applicable here. Its development would involve software engineering and hardware testing, not a data-driven training process.

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

This information is not provided and is not applicable for the reasons stated in point 8.

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