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510(k) Data Aggregation

    K Number
    K214095
    Date Cleared
    2022-08-15

    (230 days)

    Product Code
    Regulation Number
    876.1500
    Reference & Predicate Devices
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Intuitive Surgical Endoscopic Instrument Control System (da Vinci System, Model IS4000) and the Intuitive Surgical Endoscopic Instrument Control System (da Vinci X Surgical System, Model 154200) 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.

    Device Description

    The da Vinci XXi (IS4200/IS4000) 8mm EndoWrist Instruments have a unique articulating design at their distal tips the human wrist. While seated at the Surgeon Console of the Surgical System, the surgeon can precisely control movements of the end effectors/instrument tips to perform one or more specific surgical tasks e.g., grasping, suturing, cutting, or tissue manipulation.

    AI/ML Overview

    This document describes the 510(k) premarket notification for the da Vinci Xi/X (IS4200/IS4000) 8mm Reusable Instruments. The submission focuses on changes to the labeling regarding an increased number of lives (uses) and reprocessing cycles for these instruments.

    Here's a breakdown of the requested information:

    1. A table of acceptance criteria and the reported device performance

    The document does not explicitly state numerical acceptance criteria for each test in a table format, nor does it provide quantitative performance data for the subject device beyond stating that it "successfully met the acceptance criteria" or "meet the requirements." However, the general types of tests and their objectives serve as implicit acceptance criteria.

    Type of TestImplicit Acceptance Criteria (Objective)Reported Device Performance
    Cleaning ValidationEfficacy of manual and automated cleaning processes, meeting standards and guidance (FDA Guidance: "Reprocessing Medical Devices in Health Care Settings...", AAMI TIR 12:2020, AAMI TIR 30:2011/(R)2016, ANSI/AAMI ST15883-1:2009/(R)2014)"The da Vinci X/Xi 8mm Reusable Instruments successfully met the acceptance criteria for all markers. The test results demonstrate that the da Vinci X/Xi 8mm Reusable Instruments can be cleaned using the following cleaning methods: Automated cleaning process using a compatible automated washer/disinfector. Manual cleaning process using an ultrasonic bath."
    Reliability/Life TestingInstruments are not adversely affected by the increased number of lives (uses) and reprocessing cycles specified in Table 1."Reliability/Life Testing was performed to ensure that da Vinci X/Xi 8mm Reusable Instruments are not adversely affected by the increased number of lives (uses) and reprocessing cycles for these as listed in Table 1." (It implicitly states they did ensure this by performance of the test, and the conclusion states substantial equivalence, meaning criteria were met).
    Electrical Performance TestingMeet requirements within the FDA guidance, "Premarket Notification (510(k)) Submissions for Electrosurgical Devices for General Surgery," issued March 9, 2020, after multiple reuse and reprocessing cycles."This testing was performed to ensure the da Vinci X/Xi 8mm Extended Lives Instruments family (subject devices as listed in Table 1) meet the requirements within the FDA guidance, Premarket Notification (510(k)) Submissions for Electrosurgical Devices for General Surgery, Document Issued on March 9, 2020." (Implies requirements were met).
    Thermal Effects TestingThermal effects on tissue are comparable between the subject and predicate devices, in accordance with the FDA guidance, "Premarket Notification (510(k)) Submissions for Electrosurgical Devices for General Surgery," issued March 9, 2020."Thermal Effects testing was performed to confirm that thermal effects on tissue are comparable between the subject and predicate devices. The testing was performed in accordance with the FDA guidance, 'Premarket Notification (510(k)) Submissions for Electrosurgical Devices for General Surgery', Document Issued on March 9, 2020." (Implies comparability was confirmed and guidance met).

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

    The document does not specify exact sample sizes (e.g., number of instruments tested) for any of the performance tests (Cleaning Validation, Reliability/Life Testing, Electrical Performance Testing, Thermal Effects Testing). The data provenance (e.g., country of origin of the data, retrospective or prospective) is also not mentioned, but the tests described are typically prospective, laboratory-based engineering and validation tests conducted by the manufacturer.

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

    This information is not provided. The tests described are engineering and laboratory performance tests, not clinical studies involving expert interpretation of medical data (like imaging). The "ground truth" for these types of tests is generally defined by calibrated measurements against established standards and specifications, not expert consensus.

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

    Not applicable. The described tests are objective performance evaluations against predefined standards, not a review of clinical outcomes or imaging by experts.

    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. This document pertains to the reusability and safety of surgical instruments, not an AI-assisted diagnostic or interpretative device.

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

    Not applicable. This document is about surgical instruments, not an algorithm.

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

    For the performance tests:

    • Cleaning Validation: The ground truth is the absence of biological contaminants and residues, measured against specific acceptance criteria defined by industry standards (AAMI TIR 12, AAMI TIR 30, ANSI/AAMI ST15883-1) and FDA guidance for reprocessing.
    • Reliability/Life Testing: The ground truth is the continued functionality and integrity of the instruments after the increased number of uses and reprocessing cycles, as compared to design specifications.
    • Electrical Performance Testing: The ground truth is adherence to electrical safety and performance parameters outlined in FDA guidance for electrosurgical devices.
    • Thermal Effects Testing: The ground truth is comparability of thermal effects to the predicate device, consistent with FDA guidance for electrosurgical devices.

    8. The sample size for the training set

    Not applicable. There is no mention of a "training set" as this device is not an AI/ML algorithm.

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

    Not applicable, as there is no training set for an AI/ML algorithm.

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