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The HERMES® O.R. Control Center and Port Expander is indicated for use with Stryker Endoscopy 882 Camera, Stryker Quantum 5000 Light Source, Stryker SE5 Shaver, WOM 20L Insufflator, WOM 2.0L Arthroscopy Pump, Stryker Total Performance System, Berchtold Surgical Lights, Steris Amsco Table Model SP3085, Steris Amsco Table Model SP3085, AESOP®HR (HERMES-Ready™), Valleylab Force FX™ Electro-surgical Unit, Smith & Nephew Dyonics® Access 15 Arthroscopic Fluid Irrigation System, Smith & Nephew Dyonics® Vision 635 Digital Image Management System, Skytron Stellar Series O.R. Lights, S&N Dyonics® Power Shaver (K030240) and the Smith & Nephew 400 Insufflator. It can be used in general laparoscopy, nasopharyngoscopy, ear endoscopy, and sinuscopy where a laparoscope/endoscope is indicated for use. A few examples of the more common endoscopic surgeries are laparoscopic cholecystectomy, laparoscopic hernia repair, laparoscopic appendectomy, laparoscopic pelvic lymph node dissection, laparoscopically assisted hysterectomy, laparoscopic & thoracoscopic anterior spinal fusion, 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 in indicated and examination of the evacuated cardiac chamber during performance of valve replacement.

The HERMES O.R. Control Center is a computer-driven system whose basic function is to offer the surgeon the additional option of voice control for ancillary devices. The intent of the HERMES O.R. Control Center is to allow for simplified and more direct control of medical device settings by the physician, thereby eliminating the necessity of using the various interfaces existing on ancillary devices, or relying on verbal communications between the surgeon and other personnel in the operating room in order to adjust the surgical equipment.

The HERMES® O.R. Control Center is a computer-driven system providing voice control for ancillary devices in surgical settings.

Here's an analysis of the acceptance criteria and supporting studies based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

The provided document details various engineering and safety standards (e.g., IEC, EN, UL, CAN/CSA) and internal company tests (CMI System Functional Testing, Software Verification and Validation, Environmental Testing). These types of tests typically involve controlled laboratory environments and specific test configurations, rather than human subject test sets with "data provenance" in the sense of patient data. Therefore, the concept of sample size and data provenance (country of origin, retrospective/prospective) for clinical test sets is not applicable to the data presented in this submission, as it focuses on demonstrating compliance with technical and safety standards.

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

This information is not provided in the document. The tests performed are primarily engineering and hardware/software verification and validation, which rely on defined test procedures and specifications rather than expert consensus on clinical ground truth.

4. Adjudication Method for the Test Set

This information is not provided and is not applicable to the types of engineering and safety tests outlined.

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

An MRMC study was not conducted, as the submission focuses on the safety and effectiveness of a control system for medical devices, not on the interpretation of medical images or data by human readers. The device aims to simplify control during surgery, not to aid in diagnostic or interpretive tasks where an MRMC study would be relevant.

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

The submission describes device testing in terms of compliance with various technical and safety standards (e.g., electrical safety, EMC, software functionality). These demonstrate the algorithm's functionality and performance in a standalone capacity within the system's design parameters. However, it's important to note that the device's intended use is inherently with a human operator (the surgeon) in the loop, as it provides "voice control for ancillary devices" and aims to eliminate "relying on verbal communications between the surgeon and other personnel." The described tests verify the technical integrity of the standalone components. While the performance of the voice control algorithm itself would have been tested, the document does not break down specific "standalone algorithm performance" metrics separate from the broader system functional tests (e.g., VA-24772, CP-15345).

7. Type of Ground Truth Used

The ground truth for the various tests appears to be:

• Established industry standards and regulations: For tests like IEC, UL, EN, and CAN/CSA, the ground truth is compliance with the specified requirements and limits defined in those standards.
• System specifications and design documents: For CMI's internal functional, software, and environmental testing (VA-24772, CP-15345, VA-19795), the ground truth would be predetermined performance specifications, design requirements, and expected operational parameters of the HERMES® O.R. Control Center.

8. Sample Size for the Training Set

This information is not provided and is not applicable. The HERMES® O.R. Control Center is a control system that seems to rely on voice recognition and device integration. While a voice recognition component would historically have a training set, the submission does not detail its development or any machine learning training sets. Instead, the focus is on the safety, electrical, and functional performance of the integrated system.

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

This information is not provided and is not applicable.

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The HERMES® O.R. Control Center and Port Expander is indicated for use with Stryker Endoscopy 882 Camera, Stryker Quantum 5000 Light Source, Stryker SE5 Shaver, WOM 20L Insufflator System, WOM 2.0L Arthroscopy Pump, Stryker Total Performance System, Berchtold Surgical Lights, Steris Amsco Table Model SP3085, Steris Amsco Table Model SP3085, AESOP®R (HERMES-Ready™), Valleylab Force FX™ Electrosurgical Unit, Smith & Nephew Dyonics® Access 15 Arthroscopic Fluid Irrigation System, Smith & Nephew Dyonics® Vision 635 Digital Image Management System, Skytron Stellar Series O.R. Lights, S&N Dyonics® Power Shaver (K030240) and the Smith & Nephew InteliJET Arthroscopic Fluid Management System. It can be used in general laparoscopy. nasopharyngoscopy, ear endoscopy, and sinuscopy where a laparoscope/endoscope is indicated for use. A few examples of the more common endoscopic surgeries are laparoscopic cholecystectomy, laparoscopic hernia repair, appendectorny, laparoscopic pelvic lymph node laparoscopic dissection. laparoscopically assisted hysterectomy, laparoscopic & thoracoscopic anterior spinal fusion, 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 in indicated and examination of the evacuated cardiac chamber during performance of valve replacement.

The HERMES O.R. Control Center is a computer-driven system whose basic function is to offer the surgeon the additional option of voice control for ancillary devices. The intent of the HERMES O.R. Control Center is to allow for simplified and more direct control of medical device settings by the physician, thereby eliminating the necessity of using the various interfaces existing on ancillary devices, or relying on verbal communications between the surgeon and other personnel in the operating room in order to adjust the surgical equipment.

The HERMES® O.R. Control Center is a device that allows for voice control of ancillary medical devices during surgical procedures. The 510(k) summary provides information on the device's adherence to various engineering and safety standards, but it does not include a study proving device performance against specific clinical acceptance criteria in terms of AI accuracy or effectiveness.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document lists various standards the device was tested against. These are primarily safety, electrical, and software validation standards, not performance metrics related to diagnostic accuracy or clinical outcomes often seen in AI/ML medical devices. Therefore, the "reported device performance" in relation to clinical acceptance criteria is not applicable or provided in this context.

Note: This submission primarily focuses on electrical safety, electromagnetic compatibility, and software validation for a device that provides voice control functionality. It is not an AI/ML device in the modern sense that interprets data for diagnostic or predictive purposes, and therefore, typical AI acceptance criteria like sensitivity, specificity, or AUC based on clinical data are not present.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document does not describe a clinical "test set" for performance evaluation in the context of diagnostic or predictive AI. The testing mentioned refers to technical standards compliance (functional, environmental, software). Therefore, information on sample size, data provenance, or retrospective/prospective nature of a clinical test set is not applicable here.

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

As there is no clinical "test set" for evaluating diagnostic or predictive performance, there is no information on experts establishing ground truth. The device's function is voice control, and ground truth would relate to the accuracy of voice command recognition and execution, which would be covered under functional testing (VA-24772).

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

Not applicable, as no clinical test set for diagnostic/predictive performance is described.

5. 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

Not applicable. The HERMES O.R. Control Center is a voice-controlled interface for surgical equipment, not an AI system designed to assist human "readers" (e.g., radiologists interpreting images). The purpose is to streamline control, not to improve diagnostic interpretation.

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

The device itself is a "human-in-the-loop" device by its very nature, as it responds to voice commands from a surgeon. Therefore, a standalone (algorithm only) performance, in the context of interpretation, is not applicable. Its standalone performance would be its ability to accurately recognize and execute commanded actions, which would fall under functional testing.

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

For the functional aspects of the device, the ground truth would likely be the correct understanding and execution of voice commands by the system, as verified during "CMI System Functional Testing" (VA-24772) and "CMI Software Verification and Validation" (CP-15345). This is not a clinical ground truth like pathology or outcomes data, but rather a technical ground truth related to functional correctness.

8. The sample size for the training set

This device, in this submission, is not described as an AI/ML device that undergoes "training" on a dataset in the modern sense (e.g., for image recognition or predictive analytics). It's a control system with voice recognition. The "training" for such a system typically involves developing and refining the voice recognition algorithms against a collection of voice commands, but the document does not specify a training set size or methodology.

9. How the ground truth for the training set was established

Given the absence of a described "training set" as understood in current AI/ML contexts, there is no information on how ground truth for a training set was established. For voice recognition, ground truth would usually involve correctly transcribed speech paired with the associated intended action, but these details are not provided in the 510(k) summary.

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