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The nordicMEDIVA software is an advanced visualization and processing platform with a specific focus on providing algorithms designed to analyze functional and dynamic MRI data of the brain. The software runs on a server in a networked environment and is accessed by users via a standard web browser. It can communicate with other imaging platforms that support DICOM, and process medical image data acquired through DICOM-compliant imaging devices and modalities.

nordicMEDIVA is indicated for image analysis and visualization of functional and dynamic MRI data of the brain, presenting derived properties and parameters from the input image data in a clinically useful context.

nordicMEDIVA is a software as a medical device (SaMD) for processing of MR images of the brain. Users will configure analysis pipelines, which are executed automatically when image data is received or manually by a user. The user can choose to send the results to other DICOM nodes for review or use nordicView for their review and export the results to PACS, neuro navigation systems, or other DICOM-compliant modalities. nordicMEDIVA is a server-client solution and can be installed on a local server at the customer's location or in a cloud-based setup. The software is containerized with Docker technology and operates on a GPU-enabled Linux host. This allows customers to manage the server environment themselves or use it as a Software as a Service (SaaS) hosted by NordiclmagingLab AS in the cloud. Customers can install the server on physical hardware or in their own cloud infrastructure. The device comprises a database, DICOM functionality, various APIs, a visualization engine, and medical image analysis modules. The device is not intended for long-term persistent storage of medical diagnostic data. The device incorporates rule-based algorithms for the calculation of metrics from dynamic MRI data. The device does not incorporate Al algorithms based on neural networks. The device connects to other imaging modalities, such as MR scanners, PACS, and surgical navigation systems.

Here's a breakdown of the acceptance criteria and study details for the nordicMEDiVA device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance

• Sample size for test set: Not explicitly stated in the provided text.
• Data Provenance: Not explicitly stated. The text mentions "internal and external clinical experts" reviewing results, but doesn't specify the country of origin of the data or whether it was retrospective or prospective.

3. Number of Experts and Qualifications for Ground Truth

• Number of experts: Not explicitly stated. The text refers to "internal and external clinical experts."
• Qualifications of experts: Not explicitly stated, beyond being "clinical experts." Specific experience levels (e.g., "radiologist with 10 years of experience") are not provided.

4. Adjudication Method for the Test Set

The text does not specify an explicit adjudication method (e.g., 2+1, 3+1). It states "reviewed by internal and external clinical experts." This suggests a consensus-based review, but the exact process of reaching that consensus or resolving disagreements is not detailed.

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

• Was an MRMC study done? No, an MRMC comparative effectiveness study was not described. The studies focused on comparing the device's output to that of predicate devices and showing equivalence, rather than directly measuring human reader performance with and without AI assistance.
• Effect size of human reader improvement: Not applicable, as an MRMC comparative effectiveness study was not performed.

6. Standalone Performance Study (Algorithm Only)

Yes, a form of standalone performance was implicitly done. The tests for Diffusion and Tractography, BOLD fMRI, and DSC were evaluations of the nordicMEDiVA software's output (algorithm only) compared to established predicate devices. This demonstrates algorithm-only performance in generating results equivalent to cleared devices.

7. Type of Ground Truth Used

The ground truth for the test set appears to be based on:

• Predicate Device Equivalence: For Diffusion and Tractography, and BOLD fMRI, the ground truth was essentially the "equivalent results from the predicate device, nordicBrainEx (K163324)," as reviewed and confirmed by clinical experts.
• Reference Device Equivalence/Metrics: For DSC, the ground truth was based on demonstrating "the same" results as the reference device nordicMEDiVA (K241608), quantified by a Lin's Concordance Correlation Coefficient of enhancing voxels.
• Expert Confirmation: The final acceptance relied on confirmation by "internal and external clinical experts."

8. Sample Size for Training Set

The text does not provide any information regarding the sample size used for a training set. This is because the device does not incorporate AI algorithms based on neural networks. It explicitly states: "The device incorporates rule-based algorithms for the calculation of metrics from dynamic MRI data. The device does not incorporate AI algorithms based on neural networks." Therefore, there would be no "training set" in the context of deep learning models.

9. How Ground Truth for Training Set was Established

Given that the device uses "rule-based algorithms" and "does not incorporate AI algorithms based on neural networks," there would be no training set requiring ground truth establishment in the traditional machine learning sense. The performance is based on the correctness and validation of its rule-based calculations against established clinical principles and comparison to predicate device outputs.

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