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510(k) Data Aggregation

    K Number
    K082043
    Date Cleared
    2008-08-01

    (14 days)

    Product Code
    Regulation Number
    886.4370
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    CARRIAZO-PENDULAR MICROKERATOME

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

    The Carriazo-Pendular Microkeratome is indicated for shaving the cornea prior to lamellar (partial thickness) transplant or to create a flap in the cornea.

    Device Description

    The Carriazo-Pendular is an AC powered microkeratome. Cutting head and blade are designed in a convex form similar to the cornea itself. Due to the unique pendulum motion of the Carriazo-Pendular, the cornea becomes more applanated in the center than in the periphery. This technology provides a homogeneous and predictable flap thickness and smooth cutting edges. The Carriazo-Pendular consists of: Carriazo-Pendular console, Cutting heads, Suction rings, Foot switches, Monitoring software.

    AI/ML Overview

    The provided 510(k) summary for the Carriazo-Pendular Microkeratome (K082043) does not contain detailed acceptance criteria or a comprehensive study report with the level of detail requested. However, based on the available information, I can extract and infer some aspects of the performance and testing.

    Here's a breakdown of the requested information, with clear indications of what is explicitly stated and what is not:

    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance CriteriaReported Device Performance
    Substantial Equivalence to Predicate Device (Carriazo-Pendular Microkeratome K032910)"substantially equivalent in safety and effectiveness" to the legally marketed (predicate) Carriazo-Pendular (K032910). The new cutting heads perform flaps similar to existing heads, differing primarily in resulting flap thicknesses.
    Functional Performance"Functional testing...demonstrate that the device performs as intended." (Specific metrics for "performs as intended" are not provided).
    Cleaning Validation"cleaning validation...demonstrate that the device performs as intended." (Specific metrics are not provided).
    Biocompatibility"biocompatibility tests demonstrate that the device performs as intended." (Specific metrics are not provided).
    Flap Thickness (for new cutting heads)New cutting heads perform flaps "similar to the existing heads except for the different flap thicknesses." (Specific target ranges or deviations are not provided).
    Homogeneous and Predictable Flap Thickness (Claim of predicate device's technology)The device's "unique pendulum motion...provides a homogeneous and predictable flap thickness and smooth cutting edges." (This is a design claim, not a measured performance reported for this submission's testing as per the summary).
    Smooth Cutting Edges (Claim of predicate device's technology)The device's "unique pendulum motion...provides a homogeneous and predictable flap thickness and smooth cutting edges." (This is a design claim, not a measured performance reported for this submission's testing as per the summary).

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

    • Sample Size for Test Set: Not explicitly stated. The document mentions "Test on pig eyes and clinical performance data on human eyes." However, the exact number of pig eyes or human eyes is not provided.
    • Data Provenance:
      • Country of Origin: Not explicitly stated, but the manufacturer is based in Germany. Clinical data could be from Germany or elsewhere.
      • Retrospective or Prospective: Not specified.

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

    This information is not provided in the 510(k) summary. The document does not describe how ground truth was established for the "clinical performance data on human eyes" or for the pig eye tests. There is no mention of expert review or consensus for outcome assessment.

    4. Adjudication Method for the Test Set

    This information is not provided in the 510(k) summary. Given that the summary does not detail the nature of the "clinical performance data" or how experts established ground truth, an adjudication method is not described.

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

    No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study is not mentioned. This device is a microkeratome (surgical instrument), not an AI-powered diagnostic imaging device that typically involves human readers. Therefore, the concept of human readers improving with or without AI assistance does not apply in this context.

    6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study Was Done

    This question is not directly applicable as the device is a surgical instrument, not an algorithm. The "Pendular Monitoring Software" is mentioned, but its performance is described as for "read out of relevant monitored data... for quality insurance, supporting the doctor in the process of improving the cutting execution and analysis of potential cut complications." This suggests it's an assistive tool for data analysis post-procedure, not a standalone diagnostic or automated system replacing a human operator.

    7. The Type of Ground Truth Used

    This information is not explicitly stated. For the "Test on pig eyes," the ground truth would likely involve precise measurements of flap thickness and quality (e.g., smoothness of edges, consistency), potentially assessed by histology or optical coherence tomography. For "clinical performance data on human eyes," outcomes could relate to actual flap thickness achieved in surgeries, visual acuity outcomes, or complication rates, but the method for establishing the true flap characteristics against which performance was measured is not described.

    8. The Sample Size for the Training Set

    This device does not appear to involve a "training set" in the context of machine learning or algorithms that learn from data. The "Pendular Monitoring Software" collects data, but its development is not described as involving a training set in the AI sense. Therefore, the sample size for a training set is not applicable/not provided.

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

    As there is no mention of a "training set" in the context of an AI/ML algorithm learning, this question is not applicable.

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