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510(k) Data Aggregation

    K Number
    K230150
    Device Name
    OptimMRI
    Manufacturer
    RebrAIn, SAS
    Date Cleared
    2023-07-21

    (183 days)

    Product Code
    QIH,LLZ
    Regulation Number
    892.2050
    Type
    Traditional
    Panel
    Radiology
    Reference & Predicate Devices
    K213930,DEN210003,K162830
    Predicate For
    K242054
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    OptimMRI is a software application intended to aid qualified medical professionals in processing, visualizing, and interpreting anatomical structures from medical images. The software can be used to process pre-operative DICOM compatible MR images to generate 3D annotated models of the brain that aid the user in neurosurgical functional planning. The annotated MR images can further be used in conjunction with other clinical methods as an aid in localization of the Subthalamic Nuclei (STN) and Ventral Intermediate Nucleus (VIM) regions of interest.

    Device Description

    OptimMRI is a software application for processing medical images of the brain that enables 3D visualization and analysis of anatomical structures. Specifically, the software can be used to read DICOM compatible pre-operative MR images acquired by commercially available imaging devices. These images can be processed to generate 3D markers in specific regions of the anatomy to allow qualified medical professionals to display, review, analyze, annotate, interpret, export, and plan neurosurgical functional procedures. OptimMRI is used as an aid to localize regions of the brain such as Subthalamic Nuclei (STN) and Ventral Intermediate Nucleus (VIM).

    AI/ML Overview

    Here's an analysis of the acceptance criteria and study details for OptimMRI, based on the provided document:

    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance Criteria (Target Value)Reported Device Performance
    STN Localization Accuracy: At least 90% of surface distances not greater than 2.0 mm (relative to comparable software tools).STN Localization: Performance evaluation studies against reference devices Guide XT (K213930) and SureTune4 (DEN210003) demonstrated that at least 90% of surface distances of STN were not greater than 2.0 mm when using segmentation tools for OptimMRI. This result is stated to be "identical to the predicate SIS Software that used high-resolution 7T MRIs of the brain."
    VIM Localization Accuracy: At least 90% of surface distances not greater than 2.0 mm (relative to comparable software tools).VIM Localization: Performance evaluation studies against reference devices Guide XT (K213930) and SureTune4 (DEN210003) demonstrated that at least 90% of surface distances of VIM were not greater than 2.0 mm when using segmentation tools for OptimMRI. (Note: The document implies this criteria was met, similar to STN, through the phrasing "demonstrated that at least 90% of surface distances of STN or VIM were not greater than 2.0mm when using segmentation tools for OptimMRI.")

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

    • STN Study Test Set Sample Size: 44 cerebral MRIs (88 hemispheres)
    • VIM Study Test Set Sample Size: 31 cerebral MRIs (62 hemispheres)
    • Data Provenance: Retrospective (specified as "retrospectively annotated"). Country of origin is not specified in the provided text.

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

    • The document states: "Qualified and experienced medical professionals performed the segmentation".
    • The exact number of experts is not specified.
    • The specific qualifications of the experts (e.g., "radiologist with 10 years of experience") are not specified beyond "Qualified and experienced medical professionals."

    4. Adjudication Method for the Test Set

    • The adjudication method is not explicitly stated. The document mentions that "Qualified and experienced medical professionals performed the segmentation, and all validation criteria were met." This suggests consensus or an internal process but doesn't detail the specific method (e.g., 2+1, 3+1).

    5. If a Multi-reader Multi-case (MRMC) Comparative Effectiveness Study Was Done

    • No MRMC comparative effectiveness study involving human readers and AI assistance is described. The study compared OptimMRI's segmentation accuracy against other cleared commercially available comparable software tools (Guide XT and SureTune4), not against human reader performance with or without AI assistance.

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

    • Yes, the performance study describes the standalone accuracy of OptimMRI's segmentation tools (which are semi-automatic as mentioned in the "Comparison to Predicate Device" section). The comparison is directly between OptimMRI's output and the outputs of other software (Guide XT and SureTune4), indicating an algorithm-only performance evaluation against established benchmarks. The device is described as having "segmentation process is semi-automatic," implying the algorithm generates the segmentation which is then validated.

    7. The Type of Ground Truth Used

    • The ground truth used was segmentations performed by other cleared commercially available comparable software tools (Guide XT and SureTune4), which themselves are likely validated against more fundamental ground truth. The document states a comparison of "Accuracy of segmentations for OptimMRI was compared to previously cleared commercially available comparable software tools." implying these tools served as the reference for ground truth in this study.

    8. The Sample Size for the Training Set

    • The sample size for the training set is not provided in the document. The text focuses on the performance evaluation study used for 510(k) clearance.

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

    • How the ground truth for the training set was established is not provided in the document.
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