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The Intuitive Surgical Endoscopic Instrument Control System (da Vinci Surgical System, Model IS5000) shall 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 and general thoracoscopic surgical procedures. The system is indicated for adult 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.

The da Vinci Surgical System, Model IS5000, is a software-controlled, electro-mechanical system designed to enable complex surgery using a minimally invasive approach. The system consists of a Console (Surgeon Console or SSC), a Robot (Patient Side Cart or PSC), and a Tower (Vision Side Cart or VSC) and is used with an endoscope, instruments, and accessories.

The basis for this submission is the modification of the da Vinci Surgical System, Model IS5000, cleared under K232610. 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 for user to update the system. The configuration data includes 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 for the da Vinci Surgical System (IS5000), K251202, describes a submission for a modification to an already cleared medical device, not a completely novel device. The core of this submission revolves around software changes, specifically the addition of a "Networked Central Configuration Management" feature and associated cybersecurity updates.

As such, the detailed information typically found for a new device's performance study (MRMC, expert ground truth, extensive sample sizes for training/test sets, etc.) is not present in this type of 510(k) submission. The FDA clearance is based on demonstrating that the modifications do not adversely affect the device's safety and effectiveness and that the new features function as intended without introducing new risks.

Therefore, the response below will focus on what can be inferred or reasonably assumed from the provided document regarding performance criteria and how the device meets them in the context of this specific modification.

Acceptance Criteria and Device Performance for da Vinci Surgical System (IS5000) K251202

Given that this 510(k) submission (K251202) is for a modification to an already cleared device (K232610), the performance data provided focuses on verifying the safety and functionality of the changes rather than re-establishing the fundamental efficacy of the surgical system itself. The core acceptance criteria revolve around ensuring that the new software feature ("Networked Central Configuration Management") and associated cybersecurity updates do not introduce new risks or degrade the existing performance of the device.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set Sample Size: The document does not specify a quantitative "sample size" in terms of patient cases or surgical procedures for the performance testing of these software modifications. This is typical for software-only changes. Instead, testing would involve a comprehensive set of software validation tests, unit tests, integration tests, and system-level tests for the new functionalities and cybersecurity.
• Data Provenance: Not specified in terms of country of origin. The testing would have been performed by the manufacturer (Intuitive Surgical, Inc.) in a controlled development and testing environment. The data is retrospective in the sense that it's derived from internal validation activities, not a prospective clinical trial for these specific changes.

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

• Number of Experts: Not explicitly stated. For software and cybersecurity validation, "experts" would likely be:
  • Software engineers and quality assurance (QA) testers experienced in medical device software.
  • Cybersecurity experts (e.g., ethical hackers, penetration testers, network security specialists).
  • Clinical subject matter experts (e.g., surgeons, clinical engineers) might be involved in reviewing use cases and validating clinical workflows impacted by the new features, but this is not detailed.
• Qualifications of Experts: Not specified. It's assumed they would possess relevant engineering, software development, cybersecurity, and potentially clinical experience for their respective roles in the verification and validation process.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable or specified. For software and cybersecurity testing, "adjudication" typically refers to the process of resolving discrepancies in expert interpretations of data (e.g., image reads). In software testing, the "ground truth" is adherence to design specifications and user requirements, as well as absence of bugs and security vulnerabilities. Test results are compared against expected outcomes, not expert consensus.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• MRMC Study: No, an MRMC study was not performed. MRMC studies are typically conducted to evaluate the impact of a new diagnostic imaging AI algorithm on human reader performance (e.g., radiologists improving detection rates). This 510(k) is for a surgical system's software modification, not a diagnostic AI.

6. If a Standalone (Algorithm Only) Performance Study Was Done

• Standalone Performance Study: Not in the traditional sense of an AI algorithm's diagnostic performance (e.g., sensitivity/specificity for a disease). However, the "Software Testing" and "Cybersecurity Testing" sections represent the standalone performance evaluation of the new software feature and its security aspects in isolation from the full clinical context. This involves testing the software's ability to correctly manage configurations, push updates, and maintain data integrity and security, independent of a specific surgical procedure.

7. The Type of Ground Truth Used

• Type of Ground Truth: For this type of submission, the ground truth is established by:
  • Design Specifications and User Requirements: The software is tested against whether it correctly implements the defined functionalities (e.g., "Networked Central Configuration Management" remotely pushes data, updates settings as specified).
  • Cybersecurity Threat Models: The security measures are validated against identified threats and vulnerabilities to ensure they provide adequate protection.
  • Existing Validated Performance: The ground truth for the core surgical system's performance is assumed from its prior clearance (K232610), and the current testing aims to ensure this performance is not degraded.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not applicable. The "Networked Central Configuration Management" feature is a conventional software function, not a machine learning or AI algorithm that requires a "training set."

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

• Ground Truth for Training Set Establishment: Not applicable, as there is no training set for a machine learning model in this submission.

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