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510(k) Data Aggregation

    K Number
    K211997
    Date Cleared
    2021-12-10

    (165 days)

    Product Code
    Regulation Number
    876.1500
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    8 mm SureForm 30 Curved-Tip Stapler, 8 mm SureForm 30 Stapler, SureForm 30 Reloads

    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Intuitive Surgical 8 mm SureForm 30 staplers and reloads and accessories are intended to be used with a compatible da Vinci Surgical system for resection of vasculature and tissue and/or creation of anastomoses in General, Thoracic, Gynecologic, Urologic, and Pediatric surgery.

    Device Description

    The Intuitive Surgical 8 mm SureForm 30 Curved-Tip Stapler and the 8 mm SureForm 30 Stapler are disposable, fully wristed, articulating, surgical staplers and are designed for use exclusively with the Intuitive Surgical da Vinci Xi and X Surgical Systems (Models IS4000 and IS4200 Systems). The staplers are controlled by the surgeon using the Surgeon Console of the IS4000/IS4200 Systems. They are intended for resection, transection and/or creation of anastomoses in surgery. The staplers achieve their intended use by placing multiple staggered rows of implantable staples in the target tissue (stapling) followed by cutting of the target tissue along the middle of the staple line (transection). The 8 mm SureForm 30 Reloads consist of a single-use cartridge that contains four staggered rows of implantable titanium alloy (Ti3A12.5V) staples. The reloads are offered in three configurations (gray, white, and blue). Each color represents a different staple leg height and tissue gap for use with various tissue thicknesses.

    AI/ML Overview

    The provided text describes the regulatory clearance for a medical device, the 8 mm SureForm 30 Curved-Tip Stapler, 8 mm SureForm 30 Stapler, and SureForm 30 Reloads. The document primarily focuses on demonstrating substantial equivalence to a predicate device through performance testing. However, it does not describe an AI/ML-based medical device where acceptance criteria, test sets, ground truth establishment, or human reader performance are typically discussed in the context of AI model validation.

    Therefore, many of the requested elements for describing the acceptance criteria and study that proves an AI/ML device meets those criteria cannot be extracted from this document. The document details design verification (bench tests) and design validation (animal studies) for a surgical stapler, which are standard for hardware medical devices, not AI.

    However, I can extract information related to the performance studies conducted for this device and present it in a similar structure, adapting the categories where appropriate to reflect the content available.

    Here's an analysis based on the provided text, addressing the points as best as possible given the nature of the device:

    Device: 8 mm SureForm 30 Curved-Tip Stapler, 8 mm SureForm 30 Stapler, SureForm 30 Reloads

    This device is a surgical stapler, not an AI/ML device. Therefore, the questions related to AI/ML specific validation (e.g., human reader improvement with AI assistance, training set details, expert consensus for ground truth on images) are not applicable.


    1. Table of Acceptance Criteria and Reported Device Performance

    For this surgical stapler, the "acceptance criteria" are implied by the successful outcomes of the various performance tests, demonstrating that the device meets design input requirements, user needs, and intended use, and is substantially equivalent to the predicate device. The document does not provide specific numerical thresholds for "acceptance criteria" for each test but rather states that the device "met all acceptance criteria" or "demonstrated equivalent performance."

    Table: Performance Study Outcomes (Analogous to Acceptance Criteria & Performance)

    Study NameStudy PurposeImplicit Acceptance Criteria / Demonstrated Performance
    Design Verification (Bench)Evaluate physical, mechanical, electrical, UI, equipment interface, reliability, packaging, and labeling.Device met all of the bench testing acceptance criteria, demonstrating that the design output meets the design input requirements.
    Acute Testing: Staple Line PerformanceAssess staple line performance and staple formation compared to predicate.The subject device met all acceptance criteria and exhibited acceptable pass rates in the areas of transection, tissue layer approximation, hemostasis, and staple formation.
    Acute Testing: HemostasisAssess hemostasis performance compared to predicate.The subject device demonstrated equivalent hemostasis performance to the predicate device.
    Acute Testing: Leak Onset PressureAssess leak onset pressure performance on ex vivo tissue compared to predicate.All staple lines met all acceptance criteria and exhibited acceptable pass rates in leak onset pressure on thick (stomach) tissue and thin (vein) tissue.
    Acute Testing: Maximum TorqueEvaluate staple line performance at maximum SmartFire torque limits compared to predicate.The 8 mm SureForm 30 Stapler and Reloads demonstrated clinically acceptable performance and met all design specifications. The subject device demonstrated equivalent clinical performance when compared to the predicate device in all adjacent fires. No new issues of safety and efficacy were raised.
    Acute Testing: Design ValidationDesign validation testing in a clinical laboratory setting closely approximating an intraoperative use situation.The subject device met all acceptance criteria. Design Validation Testing demonstrated that the design outputs of the subject device fulfill the design input requirements and that user needs and intended uses are met.
    Chronic Testing (Lung Lobectomy, Lung Wedge Resection, Small Bowel Anastomosis, Nephrectomy, Gastrectomy)Assess device performance compared to predicate in various surgical procedures over time.In all chronic studies, staple lines passed assessments for leaks intra-operatively. All animals survived through the specified survival periods (7, 14, or 28 days+). During terminal procedures, there were no signs of bleeding or leakage at the staple lines. Staple lines were well-healed at the end of the survival period for both the subject and predicate devices.
    Human Factors EvaluationUpdated Usability Risk Analysis and conducted human factors evaluation to identify safety-related usability characteristics, hazards, and situations.The 8 mm SureForm 30 Staplers and Reloads have been found to be safe and effective for the intended users, uses, and use environments based on the results of the studies.

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

    The "test set" in this context refers to the animals used in the design validation studies and the bench tests.

    • Bench Testing (Design Verification): Not specified in terms of sample size, but implies a comprehensive set of tests.
    • Animal Studies (Design Validation):
      • Staple Line Performance: 3 canine models, 1 porcine model
      • Hemostasis: 2 porcine models
      • Leak Onset Pressure: 1 porcine model
      • Maximum Torque: 2 canine models, 1 porcine model
      • Design Validation (Acute): 2 canine models, 2 porcine models
      • Lung Lobectomy (Chronic): 8 canine models
      • Lung Wedge Resection (Chronic): 8 canine models
      • Small Bowel Anastomosis (Chronic): 8 porcine models
      • Nephrectomy (Chronic): 8 porcine models
      • Gastrectomy (Chronic): 8 canine models
    • Data Provenance: The studies are described as "simulated clinical models (animal)" performed as part of "Design Validation." There is no mention of country of origin for the data or whether it's retrospective or prospective, though animal studies for regulatory submission are typically prospective and conducted under controlled lab environments.

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

    This concept is not applicable as this is a hardware device. The "ground truth" is established by direct measurement of physical or physiological parameters (e.g., staple formation, hemostasis, leak onset pressure, absence of bleeding/leakage at staple lines, tissue healing) in the animal models, rather than expert interpretation of medical images. The studies themselves are designed and conducted by qualified scientific and medical personnel as part of the manufacturer's design validation process.


    4. Adjudication Method for the Test Set

    Not applicable. This is not an AI/ML study involving human interpretation or adjudication of outputs. The results of the tests (e.g., presence/absence of leaks, healing of staple lines) are directly observed and objectively measured.


    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 is not an AI/ML device.


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

    Not applicable. There is no algorithm, only mechanical and physical performance.


    7. The Type of Ground Truth Used

    The ground truth for this device's performance validation is based on direct physiological and physical outcomes observed in animal models and measurements from bench testing. Examples include:

    • Direct observation of staple line formation, transection, tissue approximation, and hemostasis in animal models.
    • Measurement of leak onset pressure.
    • Assessment of bleeding and leakage at staple lines during and after surgery.
    • Histological or gross examination of tissue healing post-procedure.
    • Measurements of physical specifications and performance under various mechanical and electrical tests.

    8. The Sample Size for the Training Set

    Not applicable. This is not an AI/ML device, so there is no "training set."


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

    Not applicable. As there is no AI/ML model, there is no "training set" or ground truth establishment for it.

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