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The Intuitive Surgical Endoscopic Instrument Control System (da Vinci Surgical System, 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 revasoularization. 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.

This premarket notification is for a labeling modification only, to revise the Precaution for Representative Uses statement so that it does not apply to radical prostatectomy performed using the subject devices. This labeling change is supported by real-world evidence (RWE). There are no changes to the technological characteristics or the indications for use of the cleared da Vinci Xi or X Surgical Systems (Models IS4000 and IS4200) proposed in this submission. The da Vinci Xi and X Surgical Systems, Models IS4000 and IS4200, are software-controlled, electro-mechanical systems designed for surgeons to perform minimally invasive surgery. The Model IS4000 and Model IS4200 Surgical Systems consist of a Surgeon Console, a Patient Side Cart (PSC), and a Vision Side Cart (VSC) and are used with an Endoscope, EndoWrist Instruments, and Accessories.

The provided text describes a 510(k) premarket notification for a labeling modification to the da Vinci Xi and X Surgical Systems. The modification aims to remove a precaution statement regarding outcomes related to cancer treatment, specifically radical prostatectomy (RP), since this procedure was evaluated for overall survival. The submission relies on Real-World Evidence (RWE) from a retrospective cohort study to demonstrate the non-inferiority of robotic-assisted surgical device (RASD) radical prostatectomy compared to non-RASD RP concerning overall survival.

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

1. Table of Acceptance Criteria and the Reported Device Performance

Conclusion: For all evaluated time points (5, 6, 7, 8, 9, and 10 years), the p-value for non-inferiority was less than 0.0001. The adjusted overall survival difference (RASD vs non-RASD RP) and its 95% confidence interval were consistently within the non-inferiority margin of 2.0%. This indicates that robotic-assisted radical prostatectomy demonstrated non-inferiority to non-RASD RP with respect to overall survival.

2. Sample size used for the test set and the data provenance

• Sample Size (Primary Analysis): N = 24,350 patients (18,949 RASD RP, 5,401 Non-RASD RP)
• Sample Size (Sensitivity Analysis): N = 45,215 patients
• Country of Origin: United States
• Retrospective or Prospective: Retrospective cohort study
• Data Provenance: Secondary administrative healthcare claims data from Optum Clinformatics Data Mart (CDM), which is de-identified.

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

This study used administrative healthcare claims data for overall survival (death events). The ground truth for death events would be derived directly from the claims data (e.g., death records), not from expert consensus or review of images by qualified experts. Therefore, this question is not applicable in the context of this study.

4. Adjudication method for the test set

Not applicable. The study utilized administrative claims data to determine death events, which are typically recorded facts within such datasets, rather than requiring adjudication of interpretations from multiple reviewers.

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

No, an MRMC comparative effectiveness study was not done. This study is not evaluating an AI device for diagnostic or interpretative tasks where human readers' performance would be assessed with and without AI assistance. Instead, it is an effectiveness study comparing surgical approaches (RASD vs. non-RASD) on a clinical outcome (overall survival).

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

Not applicable. This study evaluates the effectiveness of a surgical system (da Vinci Xi/X) used by surgeons, not a standalone algorithm. The device, in this context, is the surgical system itself.

7. The type of ground truth used

The ground truth used was outcomes data, specifically the occurrence of a death event to determine overall survival. This information was extracted from de-identified administrative healthcare claims data.

8. The sample size for the training set

This study is a non-inferiority comparison of surgical approaches using real-world data; it does not describe the development or training of a specific algorithm within the da Vinci Surgical System itself. The surgical system is already cleared, and this submission is for a labeling change supported by clinical outcomes data. Therefore, the concept of a "training set" in the context of machine learning model development is not directly applicable to this study as described. The study used its entire dataset (N=24,350 for primary analysis, N=45,215 for sensitivity) to compare the efficacy of two surgical methods. Propensity score (PS) methods were used for statistical adjustment, which involves modeling the probability of receiving a treatment based on observed covariates, but this is a statistical technique, not an AI model training process in the typical sense for a new device submission.

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

As explained in point 8, this study does not describe the training of a new AI algorithm. The ground truth for the outcomes (overall survival) was established through de-identified administrative healthcare claims data from Optum Clinformatics Data Mart (CDM).

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