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510(k) Data Aggregation

    K Number
    K231757
    Device Name
    Ez3D-i /E3
    Manufacturer
    Date Cleared
    2023-07-14

    (28 days)

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

    Ez3D-i is dental imaging software that is intended to provide diagnostic tools for maxillofacial radiographic imaging. These tools are available to view and interpret a series of DICOM compliant dental radiology images and are meant to be used by trained medical professionals such as radiologist and dentist.

    Ez3D-i is intended for use as software to load, view and save DICOM images from CT, panorama, cephalometric and intraoral imaging equipment and to provide 3D visualization, 2D analysis, in various MPR (Multi-Planar Reconstruction) functions.

    Device Description

    Ez3D-i v5.5 is 3D viewing software for dental CT images in DICOM format with a host of useful functions including MPR, 2-dimensional analysis and 3-dimensional image reformation. It provides advanced simulation functions such as Implant Simulation, Drawing Canal, and Implant Environ Bone Density, etc. for the benefit of effective doctor and patient communication and precise treatment planning.

    AI/ML Overview

    Here's an analysis based on the provided FDA 510(k) summary for the Ez3D-i /E3 device, specifically version 5.5:

    This document is a 510(k) summary for the Ez3D-i /E3 (v5.5) device, asserting its substantial equivalence to a previously cleared device (Ez3D-i /E3 v5.4, K222069). It does not present a standalone clinical study to prove the device meets specific acceptance criteria based on diagnostic accuracy or clinical outcomes. Instead, it relies on demonstrating that the new version is substantially equivalent to a previously cleared version and that software verification and validation tests were performed.

    Here's a breakdown of the information requested:

    1. Table of Acceptance Criteria and Reported Device Performance

    The provided document does not contain a table of specific quantitative acceptance criteria related to diagnostic performance or clinical effectiveness, nor does it report such performance metrics. The approval is based on substantial equivalence and software verification/validation.

    Instead, the summary implies the "acceptance criteria" are related to successful software verification and validation, ensuring the new features function as intended and the overall device maintains the same functionality and safety profile as its predicate.

    Acceptance Criteria (Implied)Reported Device Performance (Implied)
    Software functionality as per specificationsPassed all tests based on pre-determined Pass/Fail criteria.
    Consistency with predicate device's intended use and technical characteristicsDemonstrated substantial equivalence in intended use, functionalities (operation software, computer platform, etc.), and image processing features. Differences (e.g., UI adjustments, specific tool functions) did not raise new safety concerns.
    Safety and Effectiveness maintainedModifications were not significant and did not raise any new or potential safety risks or questions of safety/effectiveness.

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

    • Test Set Sample Size: Not explicitly stated for any clinical performance or diagnostic accuracy test. The document refers to "SW verification/validation and the measurement accuracy test," implying internal testing rather than a clinical study with a patient dataset.
    • Data Provenance: Not specified, as no clinical study with patient data is detailed. The tests mentioned are likely internal software tests.

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

    Not applicable / Not specified. Since no clinical study or diagnostic performance assessment is detailed, there's no mention of experts establishing a ground truth for a test set. The device is a viewing and analysis tool, and its outputs are for interpretation by trained medical professionals.

    4. Adjudication Method for the Test Set

    Not applicable / Not specified. No clinical test set requiring adjudication is described.

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

    No, an MRMC comparative effectiveness study was not reported in this 510(k) summary. The submission focuses on substantial equivalence based on technical characteristics and software changes, not on comparing human reader performance with and without the AI (or specific features) of the device.

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

    No. The device, Ez3D-i /E3, is described as "dental imaging software that is intended to provide diagnostic tools" and "meant to be used by trained medical professionals such as radiologist and dentist." It is explicitly stated that "Results produced by the software's diagnostic, treatment planning and simulation tools are dependent on the interpretation of trained and licensed radiologists, clinicians and referring physicians as an adjunctive to standard radiology practices for diagnosis." This confirms it is not intended for standalone, algorithm-only performance.

    7. The Type of Ground Truth Used

    Not applicable / Not specified for any diagnostic performance. The ground truth for the software's functionality would be its design specifications, against which its operational performance was verified.

    8. The Sample Size for the Training Set

    Not applicable / Not specified. This device is described as "dental imaging software" with "diagnostic tools" and "advanced simulation functions" primarily for viewing, analysis, and processing. There is no mention of machine learning or AI that would require a "training set" in the context of deep learning algorithms. It appears to be a rule-based or conventional image processing software.

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

    Not applicable / Not specified, as there is no mention of a training set for machine learning.

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