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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.

OptimMRI (v2) 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 brain to allow qualified medical professionals to display, review, analyze, annotate, interpret, export, and plan neurosurgical functional procedures. OptimMRI (v2) is used as an aid to localize regions of the brain such as Subthalamic Nuclei (STN) and Ventral Intermediate Nucleus (VIM) using advanced image processing techniques and machine learning models trained on a proprietary clinical database. The software supports workflow for creating pre-operative plans prior to carrying out the intraoperative procedure. OptimMRI (v2) is configured as web-based software and its output is compatible with neurosurgical planning software supporting 3D DICOM format. Three models have been implemented within OptimMRI (v2) to segment the following regions of interest of the brain: -STN region of interest (STN itself) -Inferior part of the Ventral Intermediate Nucleus (VIM) and Zona Incerta -Inferior-lateral part of the Ventral Intermediate Nucleus (VIM)

Acceptance Criteria and Study for OptimMRI (v2)

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample Size for Test Set and Data Provenance:

The document does not explicitly state the sample size used for the test set in this specific submission for OptimMRI (v2). It mentions using "the same test methods as used for the previously cleared predicate device" and that the "performance evaluation studies demonstrated..." implies a study was conducted but the number of cases is not provided.

The document does not specify the country of origin of the data or whether it was retrospective or prospective. It only states that the machine learning models were "trained on a proprietary clinical database."

3. Number of Experts and Qualifications for Ground Truth Establishment (Test Set):

The document does not specify the number or qualifications of experts used to establish the ground truth for the test set. It refers to comparing the device's output to "reference devices Guide XT (K213930) and SureTune4 (DEN210003)," which are presumably established and validated tools for VIM localization. This implies that the ground truth for the test set was established by these reference devices' outputs, rather than by direct expert consensus on each individual case of the test set for this submission.

4. Adjudication Method (Test Set):

The document does not describe a specific adjudication method like 2+1 or 3+1 for the test set. The evaluation was based on a direct comparison of the device's output (3D markers / surface distances) to the "reference devices" (Guide XT and SureTune4).

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

No MRMC comparative effectiveness study is described in the provided text. The submission focuses on the standalone performance of the updated OptimMRI (v2) model against established reference devices, rather than evaluating human reader improvement with or without AI assistance.

6. Standalone Performance Study:

Yes, a standalone performance study was done. The document states, "STN and VIM region of interest (ROI) annotation accuracy for the subject device was validated using the same performance test protocol and acceptance criteria as the predicate OptimMRI (K230150)." This indicates that the algorithm's performance was evaluated on its own, comparing its output to that of the reference devices.

7. Type of Ground Truth Used:

The ground truth used for the performance evaluation appears to be based on the output of reference devices (Guide XT (K213930) and SureTune4 (DEN210003)). The comparison was made by measuring "surface distances of inferior-lateral regions of VIM structure" against these established tools.

8. Sample Size for Training Set:

The document mentions that the machine learning models were "trained on a proprietary clinical database," but it does not specify the sample size of this training set.

9. How Ground Truth for Training Set Was Established:

The document states that the machine learning models were "trained on a proprietary clinical database" and used "advanced image processing techniques and machine learning models." However, it does not explicitly detail how the ground truth for this training set was established. While it can be inferred that expert annotations or validated reference methods would have been crucial for training, the specific methodology is not described in this document.

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