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510(k) Data Aggregation

    K Number
    K030598
    Manufacturer
    Date Cleared
    2004-01-23

    (332 days)

    Product Code
    Regulation Number
    N/A
    Reference & Predicate Devices
    Why did this record match?
    Reference Devices :

    K012571, K022780

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

    The SISS d.b.a. MediSISS™ Reprocessed Ultrasonic Surgical Instruments are intended for soft tissue incisions when bleeding control and minimal thermal injury are desired. The instruments can be used as an adjunct to or substitute for electrosurgery, lasers, and steel instruments. The instruments are used in general, pediatric, thoracic, gynecologic, urologic, and other open and endoscopic surgeries for the transection, dissection, and coagulation of tissue(s).

    Device Description

    Reprocessed Ultrasonic Surgical Instruments consist of hand-manipulated devices, with or without rotation capability, with or without cutting ability.

    The handpiece handles are connected to the distal end-effector by a narrow-diameter metal barrel or shaft. The distal end of the device consists of a scalpel with a variety of end configurations including Flat, Cutting, and Blunt or a combination of the same. The devices are designed to be inserted through an appropriately sized trocar sleeve or cannula or used to be used in open surgery. The end-effectors are usually operated by the handpiece handles. The handles may be designed to be suppressed and released to activate the instruments end-effector. Similarly the handle may incorporate a button or switch to activate the end-effectors.

    The device's shaft may be designed to (depending on the device model and type) be rotated (up to 360°) either direction (by manipulating controls located on the handle.)

    AI/ML Overview

    This document describes the regulatory clearance for reprocessed ultrasonic surgical instruments, not a study evaluating an AI device or a new medical device's performance. Therefore, most of the requested information regarding acceptance criteria, sample sizes, expert involvement, and comparative effectiveness studies is not applicable.

    The document discusses the substantial equivalence of reprocessed devices to legally marketed predicate devices. The "performance data" section focuses on validation of the reprocessing methods (cleaning, sterilization, packaging) and demonstrating that the reprocessed devices maintain their mechanical function.

    Here's a breakdown of the relevant information from the provided text, and an explanation of why other requested information is not applicable:

    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance Criteria (for Reprocessed Devices)Reported Device Performance (Summary)
    Sterilization Validation:Complies with ANSI/AAMI/ISO 11135-1994 (Medical Devices—Validation and Routine Control of Ethylene Oxide Sterilization) and ANSI/AAMI/ISO 10993-7:1995 (Biological Evaluation of Medical Devices-Part 7: Ethylene oxide sterilization residual).
    Cleaning Validation:Complies with AAMI RDS0TIR No. 12-1994 (Designing, Testing, and Labeling Reusable Medical Devices for Reprocessing in Health Care Facilities) and FDA Reviewer Guidance (1996) for Labeling Reusable Medical Devices.
    Mechanical Testing:Undergoes mechanical testing to demonstrate that parts do not change in function.
    Visual Inspection:100% visual inspection of all products prior to packaging, labeling, and sterilization.
    Substantial Equivalence:Devices are safe, effective, and substantially equivalent to predicate devices based on similarities in functional design, materials, indications for use, and methods of construction.

    Explanation of Non-Applicability for other sections:

    The document focuses on the regulatory clearance of reprocessed medical devices (ultrasonic surgical instruments), not the development or evaluation of a new diagnostic or therapeutic device, especially not one involving AI. Therefore, the traditional "acceptance criteria" and "study" framework relevant to a novel device's performance often includes metrics like sensitivity, specificity, AUC, etc., which are not present here.

    • 2. Sample size used for the test set and data provenance: Not applicable. The document discusses process validations for cleaning, sterilization, and mechanical function, not a test set for diagnostic accuracy or clinical outcomes of a novel device. The "data" refers to validation reports from these processes.
    • 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth in this context would be related to the effectiveness of the reprocessing steps (e.g., sterility, cleanliness, mechanical integrity), which are assessed through standardized validation protocols, not expert consensus on diagnostic images or clinical conditions.
    • 4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. There is no "test set" in the sense of a set of cases requiring adjudication for a diagnostic outcome.
    • 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 is about reprocessed surgical instruments, not an AI-powered diagnostic tool.
    • 6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable. There is no algorithm or AI component in the described device or its reprocessing validation.
    • 7. The type of ground truth used (expert concensus, pathology, outcomes data, etc): For the reprocessing validation, the "ground truth" is defined by the established standards and methods for assessing sterility, cleanliness, and mechanical function. For example, sterility is confirmed by microbiological tests according to established protocols, and mechanical integrity by predefined engineering tests. This is not 'expert consensus' on clinical data, pathology, or outcomes data in the usual sense for a diagnostic device.
    • 8. The sample size for the training set: Not applicable. There is no AI model or "training set" involved.
    • 9. How the ground truth for the training set was established: Not applicable.
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