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The Intuitive Surgical Endoscopic Instrument Control System (da Vinci Xi and X Surgical Systems, Models IS4000 and IS4200) is intended to assist in the accurate control of Intuitive Surgical Endoscopic Instruments including rigid endoscopes, blunt and sharp endoscopic dissectors, scissors, scalpels, forceps/pick-ups, needle holders, endoscopic retractors, electrocautery and accessories for endoscopic manipulation of tissue, including grasping, cutting, blunt and sharp dissection, approximation, ligation, electrocautery, suturing, and delivery and placement of microwave and cryogenic ablation probes and accessories, during urologic surgical procedures, general laparoscopic surgical procedures, gynecologic laparoscopic surgical procedures, general thoracoscopic surgical procedures and thoracoscopically-assisted cardiotomy procedures. The system can also be employed with adjunctive mediastinotomy to perform coronary anastomosis during cardiac revascularization. The system is indicated for adult and pediatric use. It is intended to be used by trained physicians in an operating room environment in accordance with the representative, specific procedures set forth in the Professional Instructions for Use.

Intended Use: To assist in the accurate control of endoscopic instruments in minimally invasive surgery.

The da Vinci Xi/X Surgical Systems, Models IS4000/IS4200, are software-controlled, electro-mechanical systems designed to enable complex surgery using a minimally invasive approach. The systems consist of a Surgeon Console, a Patient Side Cart (PSC), and a Vision Side Cart (VSC) and are used with an endoscope, instruments, and accessories.

The basis for this submission is the modification of the da Vinci Xi/X Surgical Systems, Models IS4000/IS4200, cleared in K240723. The device software is being modified to include a new feature called "Networked Central Configuration Management" which enables Intuitive technical support to remotely push configuration data to a system connected to the network, including customer site preference settings and system feature enabling or disabling. The modification also includes changes to the device labeling.

The provided FDA 510(k) clearance letter pertains to the da Vinci Xi/X Surgical Systems (IS4000/IS4200). This document primarily focuses on a software modification, specifically the "Networked Central Configuration Management" feature, and does not describe a clinical study comparing device performance against specific acceptance criteria in the manner typically seen for diagnostic or AI-driven devices.

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

Key Takeaway: The submission is for a software modification to an already cleared device, not a new device requiring extensive clinical performance studies against defined acceptance criteria. Therefore, many of the requested data points (like sample size for test sets, expert consensus, MRMC studies, standalone performance with ground truth) are not applicable in this context.

1. Table of Acceptance Criteria and Reported Device Performance

Not directly applicable as described for clinical performance. This submission is for a software modification to an existing, already cleared device. The "performance data" discussed relates to software and cybersecurity testing, not clinical outcomes against specific performance metrics for diagnosis or treatment efficacy in patients compared to a predicate or ground truth.

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

Not applicable. The document describes software and cybersecurity testing, not a clinical test set with patient data for performance evaluation. Therefore, there is no mention of a sample size for a test set or data provenance (country of origin, retrospective/prospective).

3. Number of Experts Used to Establish Ground Truth and Qualifications

Not applicable. Ground truth in the context of clinical performance is not relevant for a software update related to configuration management and cybersecurity. Software testing would involve software engineers and cybersecurity experts, but their roles are not defined as "establishing ground truth" in the clinical sense.

4. Adjudication Method

Not applicable. Adjudication methods (e.g., 2+1, 3+1) are used in clinical studies, particularly for diagnostic devices, to resolve disagreements among experts in establishing ground truth. This is not mentioned as it's a software modification submission.

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

No, an MRMC study was not done. The document does not mention any clinical studies, and therefore no MRMC comparative effectiveness study was performed to evaluate human reader improvement with or without AI assistance. The da Vinci system is a surgical robot, not an AI diagnostic tool for image interpretation or similar tasks that would typically involve MRMC studies.

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

Not applicable. The da Vinci Xi/X Surgical Systems are surgical robots intended to be used with human surgeons (human-in-the-loop). A standalone performance evaluation in the absence of human involvement would not be relevant for this device. The software update is for configuration management, not an autonomous function.

7. Type of Ground Truth Used

Not applicable in the clinical sense. The "ground truth" for software testing would be the predefined functional requirements and specifications, and for cybersecurity, it would be adherence to security protocols and protection against identified threats. This is different from clinical ground truth such as pathology, expert consensus on imaging, or patient outcomes data.

8. Sample Size for the Training Set

Not applicable. The document refers to software modifications and testing for an existing device. There is no mention of a "training set" in the context of machine learning or AI model development which would require such a set. The "training" implied is for the system's internal functions, not for a learning algorithm.

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

Not applicable. As there is no mention of a training set for an AI/machine learning model, the method for establishing its ground truth is not discussed.

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