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510(k) Data Aggregation

    K Number
    K172513
    Device Name
    3D Echo
    Manufacturer
    Date Cleared
    2017-09-14

    (24 days)

    Product Code
    Regulation Number
    892.2050
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    JointVue's 3D Echo is a software application for the display and 3D visualization of ultrasound volume data derived from the Sonix Ultrasound Scanner. It is designed to allow the user to observe images and perform analyses of musculoskeletal structures using the ultrasound volume data acquired with the Sonix Ultrasound Scamer. Typical users of this system are trained medical professionals including physicians, nurses, and technicians.

    Device Description

    JointVue's 3D Echo is a software application that uses the raw ultrasound signals generated from an imaging ultrasound machine to visualize musculoskeletal structures in three dimensions. The 3D Echo software is pre-loaded on one of the following two ultrasound systems: 1) SonixOne, a tablet-based system; or 2) SonixTouch Q+ with linear transducer (BK Ultrasound model L14-5/38 GPS) and driveBAY™ tracking unit (Ascension Technology Corporation). There are also two electromagnetic (EM) sensors (Ascension 6DOF sensors, model 800, part #600786) included with the JointVue 3D echo software to identify the relative location of the ultrasound probe. Finally, there is a foot switch (stuete model MKF-2-MED GP25) included as an input device. The major software functions of the JointVue 3D Echo system include the following: 1) the ability to display axial, sagittal, coronal and oblique 2D images; 2) the ability to display the 3D surface of musculoskeletal structures; 3) the ability to display axial images with 3D visualization; and 4) the ability to provide contouring and US image visualization. The device is intended to be used in a clinical or hospital environment. JointVue's 3D Echo ultrasound system utilizes raw ultrasound signals to detect tissue interfaces and visualize joint anatomy in three dimensions. The system provides clinicians with three-dimensional models of the joint anatomy.

    AI/ML Overview

    The provided document is a 510(k) premarket notification for the 3D Echo device by JointVue, LLC. It outlines the FDA's determination of substantial equivalence to a predicate device, but it does not contain the detailed acceptance criteria or a specific study proving the device meets those criteria in the format requested.

    The document states that "Performance Testing" demonstrated equivalent precision and accuracy based on benchtop testing using a phantom. However, it does not provide the specific acceptance criteria, the detailed results of this testing, or the methodology for evaluating accuracy and precision.

    Here's an attempt to answer your questions based only on the information provided, highlighting what is missing:


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

    Acceptance CriteriaReported Device Performance
    Precision and Accuracy (implied from predicate equivalence statement)Equivalent precision and accuracy to the predicate device, "5D Viewer" (K161955), based upon benchtop testing using a phantom.
    Software Verification and ValidationDemonstrated safety and efficacy. Potential hazards classified as a moderate level of concern (LOC). Specific performance metrics are not provided.
    Mechanical and Acoustic PerformanceDemonstrated safety and effectiveness with the same accuracy and precision as the predicate device (based on benchtop testing using a phantom). Specific performance metrics are not provided.

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

    • Sample Size for Test Set: Not specified. The document only mentions "benchtop testing using a phantom."
    • Data Provenance: Not specified. The testing was described as "benchtop," implying laboratory testing, not human data.

    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)

    Not applicable. The ground truth for benchtop phantom testing would likely be based on the known physical properties or measurements of the phantom itself, not expert interpretation.


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

    Not applicable. This typically refers to medical image interpretation by multiple experts, which was not the nature of the described performance testing.


    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

    No. The document explicitly states: "Clinical testing was not required to demonstrate the safety and effectiveness of the 3D Echo software." Therefore, no MRMC study was performed.


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

    Yes, in a sense. The described "benchtop testing using a phantom" and "Software verification and validation testing" would fall under standalone performance assessment. However, the exact metrics and how "accuracy and precision" were quantified are not detailed. The device itself is described as a "software application for the display and 3D visualization," implying its performance as an algorithm is what was assessed.


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

    For the benchtop testing using a phantom, the ground truth would likely be the known physical dimensions, geometries, or simulated conditions of the phantom. No human-derived ground truth (like expert consensus, pathology, or outcomes) was used.


    8. The sample size for the training set

    Not specified. The document does not describe the development or training of any machine learning model, nor does it specify a training set size.


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

    Not specified, as no training set or machine learning development is detailed in the provided text.


    Summary of what's missing:

    The document focuses on the regulatory clearance process and establishing substantial equivalence rather than providing a detailed technical report of performance studies. Key details like specific numerical acceptance criteria, the methodology of phantom testing, and quantitative results of accuracy and precision are absent from this regulatory summary.

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