Search Filters

Search Results

Found 1 results

510(k) Data Aggregation

    K Number
    K183620
    Date Cleared
    2019-06-06

    (162 days)

    Product Code
    Regulation Number
    888.3030
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    PROSTEP TBC Implant System

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

    The PROSTEP TBC Implant System is indicated for bone reconstruction. Examples include:

    • · Bi-Cortical osteotomies in the foot
    • Distal metatarsal osteotomies
    • · Fixation of osteotomies for Hallux Valgus treatment (such as Transverse, Chevron, etc.)
    Device Description

    The PROSTEP TBC Implant System is intended for use in bone reconstruction and osteotomy of the first metatarsal. The implants are provided sterile and consist of one PROSTEP TBC implant, one MICA screw, and one ORTHOLOC 3Di screw. Based on patient anatomy and surgeon's needs, different component sizes can be selected.

    AI/ML Overview

    This document describes the PROSTEP TBC Implant System, a medical device for bone reconstruction and osteotomy of the first metatarsal. It is a 510(k) summary, which means no specific acceptance criteria or study details regarding device performance against such criteria are provided in the given text.

    The document focuses on demonstrating substantial equivalence to predicate devices based on technological characteristics and non-clinical evidence, rather than presenting a study proving performance against defined acceptance criteria.

    Therefore, many of the requested details cannot be extracted from this document, as they pertain to clinical or performance studies that are not included here.

    Here's what can be inferred from the provided text:

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

    The document does not provide a table of acceptance criteria nor does it report specific device performance metrics against such criteria. The submission relies on demonstrating substantial equivalence to predicate devices through technological characteristics and non-clinical testing.

    2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

    This information is not provided in the document. The substantial equivalence is based on non-clinical testing (static and fatigue construct testing, bacterial endotoxin assessments), not a clinical test set.

    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

    This information is not provided. No clinical test set with ground truth established by experts is mentioned.

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

    This information is not provided. No clinical test set with adjudication is mentioned.

    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

    There is no mention of an MRMC comparative effectiveness study, AI assistance, or human readers in this document.

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

    This device is an implantable system, not an AI or algorithm-based device. Therefore, a standalone algorithm performance study is not applicable and not mentioned.

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

    As no clinical study with a test set is described, no specific ground truth type is mentioned. For the non-clinical testing, the "ground truth" would be the engineering specifications and performance standards for static, fatigue, and bacterial endotoxin assessments.

    8. The sample size for the training set

    This is not applicable as the document describes a physical implant system, not a machine learning model.

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

    This is not applicable as the document describes a physical implant system, not a machine learning model.

    Summary based on available information:

    DetailInformation from Document
    Acceptance Criteria and Reported Device PerformanceNot explicitly stated in terms of specific performance metrics against defined acceptance criteria for a study. The document focuses on establishing substantial equivalence based on technological characteristics and non-clinical testing. It states: "The subject was evaluated through static and fatigue construct testing and bacterial endotoxin assessments to support the safety and effectiveness of the subject device system." and "The design characteristics of the subject device do not raise any new types of questions of safety or effectiveness and testing shows no new worst case. From the evidence submitted in this 510(k), the subject devices can be expected to perform at least as well as the predicate and are substantially equivalent."
    Sample size for test set & data provenanceNot applicable for a clinical test set; non-clinical testing (static, fatigue, bacterial endotoxin) was performed, but sample sizes for these tests are not provided. Data provenance is not mentioned.
    Number of experts & qualifications for ground truth (test set)Not applicable; no clinical test set requiring expert ground truth is described.
    Adjudication method (test set)Not applicable; no clinical test set requiring adjudication is described.
    MRMC comparative effectiveness study & effect size with/without AI assistanceNot applicable; no MRMC study or AI component is mentioned.
    Standalone (algorithm only) performanceNot applicable; this is a physical implant system, not an algorithm.
    Type of ground truth usedFor non-clinical testing, the "ground truth" would be engineering specifications and performance standards for static and fatigue testing, and accepted methodologies for bacterial endotoxin assessments. No clinical ground truth (e.g., pathology, outcomes data) is used because no clinical study is presented.
    Sample size for training setNot applicable; this is a physical implant system, not a machine learning model.
    How ground truth for training set was establishedNot applicable; this is a physical implant system, not a machine learning model.
    Ask a Question

    Ask a specific question about this device

    Page 1 of 1