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510(k) Data Aggregation

    K Number
    K140713
    Device Name
    PLANMECA ROMEXIS
    Manufacturer
    Date Cleared
    2014-06-16

    (87 days)

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

    Planmeca Romexis is a medical imaging software, and is intended for use in dental and medical care as a tool for displaying and visualizing dental and medical 2D and 3D image files from imaging devices, such as projection radiography and CBCT. It is intended to retrieve, process, render, diagnose, review, store, print, and distribute images.

    It is also a preoperative software application used for the simulation and evaluation of dental implants. The software includes monitoring features for Planmeca devices for maintenance purposes. It is designed to work as a stand-alone or as an accessory to Planmeca imaging and Planmeca dental unit products in a standard PC. The software is for use by authorized healthcare professionals.

    Planmeca Romexis software is:

    • Not intended for capturing optical impressions for dental restorations.
    • Not intended for optically scanning stone models and impressions for dental restorations.
    • Not intended for optically scanning intraoral preparations for use in designing implants and/or abutments.
    • Not intended for optically scanning intra-orally for use in orthodontics.
    • Not intended for mammography use.
    Device Description

    Planmeca Romexis is a modular imaging software for dental and medical use. It is divided into modules to provide user access to different workflow steps involving different diagnostic views of images. Patient management screen with search capabilities lets users to find patients and identify correct patient file before starting work with a patient. After creating or selecting patient, new images can be acquired using select Planmeca X-ray units.

    Planmeca Romexis is capable of processing and displaying 2D images in different formats and 3D CBCT images in DICOM format. 3D CBCT images can be viewed in near real-time multi projection reconstruction (MPR) views. 2D and 3D image browsers are provided to allow user access to relevant images. Typical image enhancement filters and tools are available to assist the user in making diagnosis but original exposure is always kept in the database for reference.

    AI/ML Overview

    The submitter, Planmeca Oy, describes the Planmeca Romexis, a modular imaging software for dental and medical use.

    Here's the breakdown of acceptance criteria and the study that proves the device meets them:

    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance Criteria (Implied)Reported Device Performance
    Stability and Operating as Designed"Testing confirmed that Planmeca Romexis is stable and operating as designed."
    Hazard Evaluation and Risk Reduction"Testing also confirmed that Planmeca Romexis has been evaluated for hazards and that the risk has been reduced to acceptable levels."
    Equivalence in Essential Functions (Bench Testing)"This confirms that both software applications are equally effective in performing the essential functions and provide substantially equivalent clinical data." (Comparison to predicate InVivoDental)

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

    The document does not specify a sample size for a test set in the traditional sense of a clinical study with patient data. The "non-clinical test results" section describes bench testing and quality assurance measures.

    • Test Set Nature: The testing performed was a "bench-testing to compare with predicate software" and involved assessing the software's stability, functionality, and hazard reduction. It did not involve a test set of patient images for diagnostic performance evaluation or a clinical trial.
    • Data Provenance: Not applicable as it was bench testing of software, not analysis of patient data.

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

    Not applicable. The testing described is non-clinical bench testing and quality assurance, not a study involving expert readers establishing ground truth for diagnostic image interpretation.

    4. Adjudication Method for the Test Set

    Not applicable, as no human reader studies with specific adjudication methods are described.

    5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

    No. The document does not describe a multi-reader multi-case (MRMC) comparative effectiveness study. The evaluation focused on bench testing against a predicate device.

    6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

    The evaluation was of the software itself in its intended functionality, which includes processing and displaying images for human interpretation. The "bench-testing to compare with predicate software" was a standalone evaluation of the software's rendering capabilities and effectiveness in performing essential functions compared to another software. It was not measuring an AI algorithm's diagnostic performance in isolation.

    7. Type of Ground Truth Used

    The "ground truth" for the non-clinical testing was based on:

    • Design Specifications/Requirements: Software was tested against its defined requirements and design.
    • Predicate Device Performance: Equivalence was established by comparing images rendered by Planmeca Romexis with those rendered by the predicate software (InVivoDental). The implicit ground truth here is the established and accepted performance of the predicate device.

    8. Sample Size for the Training Set

    Not applicable. The Planmeca Romexis is described as an "imaging software" that processes and displays images, not an AI/ML algorithm that is trained on a dataset. The document does not mention any machine learning components requiring a training set.

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

    Not applicable, as no training set for a machine learning model is mentioned or implied.

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