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510(k) Data Aggregation

    K Number
    K221558
    Manufacturer
    Date Cleared
    2022-11-15

    (168 days)

    Product Code
    Regulation Number
    888.3030
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The ALPHALOK™ Plating System bone plates, screws, and washers are intended for use in bone fractures, osteotomies, and fixation of bones and bone fragments in the upper and lower extremities, primarily of the hand, wrist, foot, ankle, and digits. Specific examples include:

    • · Forefoot, Midfoot, and Hindfoot Osteotomies
    • · Metatarsals and Metacarpals Corrections and Osteotomies
    • · Stabilization and Fixation of Metatarsal and Metacarpal Fractures
    • · Stabilization and Fixation of Ankle Fractures
    • · Syndesmosis Joint Stabilization
    • · Arthrodesis of Metatarsophalangeal (MTP) and Metacarpophalangeal (MCP) joints
    • · Flatfoot and Cavus Foot Corrections
    • · Charcot Fixation
    Device Description

    The ALPHALOK™ Plating System, consisting of the ALPHALOK™ Met Fx and ALPHALOK™ Recon, is a multi-indication reconstruction solution providing polyaxial locking technology and lowprofile design.

    The ALPHALOK™ Met Fx contains various sizes of non-sterile straight plates, T plates, Y plates, Jones plates, mini plates, and locking and non-locking screws.

    The ALPHALOK™ Recon contains various sizes of non-sterile fusion plates, Lapidus plates, peanut plates, straight plates, tab plates, T-plates, MTP plates, Cotton & Evans plates, utility plates, and Ankle Fracture plates along with locking and non-locking screws, and washers.

    All implant components are manufactured from titanium (Ti-6A1-4V, ASTM F136).

    Specific instrumentation including wires, drills, torx drivers, and drill guides are required for use with the system. The ALPHALOK™ instruments are manufactured from stainless steel.

    AI/ML Overview

    The provided text describes a medical device, the ALPHALOK™ Plating System, and its FDA 510(k) clearance. However, it does not contain any information regarding the acceptance criteria for an AI/ML medical device, nor does it describe a study proving such a device meets those criteria.

    The document focuses on the substantial equivalence of the ALPHALOK™ Plating System to predicate devices, which is a regulatory pathway for traditional medical devices (bone plates, screws, and washers). The performance data mentioned (static four-point bend testing, torsional properties testing, axial pullout calculations, driving torque testing, engineering calculations, and cleaning/sterilization validations) are all entirely mechanical and material property tests relevant to the physical components of the plating system, not to an AI/ML algorithm.

    Therefore, I cannot fulfill your request to describe the acceptance criteria and a study proving an AI/ML device meets those criteria based on the provided input. The document is about a hardware medical device, not a software/AI one.

    To answer your specific questions, the information is not present in the provided text:

    1. A table of acceptance criteria and the reported device performance: Not available, as it's a hardware device clearance, not an AI/ML performance study.
    2. Sample sized used for the test set and the data provenance: Not applicable in the context of an AI/ML test set. The performance data refers to mechanical bench testing.
    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for an AI/ML device would typically involve expert annotations of medical images or data. This document doesn't discuss AI or image analysis.
    4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
    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.
    6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.
    7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable to this type of device clearance.
    8. The sample size for the training set: Not applicable.
    9. How the ground truth for the training set was established: Not applicable.
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