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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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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 Service (SaaS) hosted by NordicimagingLab AS in the cloud. Customers can install the server on physical hardware, virtual machines, 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 AI algorithms based on neural networks.

The device connects to other imaging modalities, such as MR scanners, PACS, and surgical navigation systems.

The following modules provide the main functions of the device.

nordicView: A browser-based user interface accessed from desktop clients that provides tools for general image visualization, export, and relevant analysis tools for BOLD-fMRI and DSC-perfusion.

nordicBOLD: BOLD task-based fMRI anall magnetic susceptibility changes in the human brain in areas with altered blood flow resulting from neuronal activity. The image processing requires the definition of a so-called design matrix which is used to calculate voxel-wise statistics conveying information about the probability of the execution of the qiven task.

The design matrix is is defined such that the task or stimulation that was presented to the patient during the scan. The task or stimulation presented during scan time is often refered to as "the paradigm". The design matrix can be defined manually by the user, or a paradigm from nordicAktiva - another product from NordicNeuroLab - can be used.

nordicAktiva is a software, marketed by NordicNeuroLab, that may be used during scan time to present the patient or subject being scanned. The use of nordicAktiva is not required.

nordicDSC: Calculations of perfusion-related parameters that provide information about the blood vessel structure and characteristics. Such maps include blood volume, blood flow, time to peak, mean transit time, and leakage.

Platform: The platform includes a database, DICOM functionality, various APls, processing pipelines for the medical image analysis modules. Serves as a backbone component for the other modules of nordicMEDiVA.

Dashboard: a browser-based user interface accessed from desktop clients for administration and configuration.

The provided text describes the acceptance criteria and the study that proves the device meets those criteria for the nordicMEDiVA software.

Here's the breakdown:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not explicitly state the numerical sample size for the test set. For nordicBOLD and nordicDSC, the comparison was made against existing predicate devices with existing data. The data provenance (country of origin, retrospective/prospective) is also not specified beyond the fact that it was compared against previously cleared devices.

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

• nordicBOLD: "internal and external clinical experts" were used to review the results. The exact number and specific qualifications (e.g., "radiologist with 10 years of experience") are not specified.
• nordicDSC: The ground truth for nordicDSC seems to be based on direct comparison to the predicate device (nordicDSC K212720) through a statistical measure (Lin's CCC), rather than expert adjudication of a new test set.

4. Adjudication Method for the Test Set

• nordicBOLD: Reviewed by "internal and external clinical experts." While experts were involved, a specific adjudication method (e.g., 2+1, 3+1 consensus) is not explicitly stated. It implies a qualitative "proven to be as effective" rather than a strict quantitative adjudication.
• nordicDSC: Adjudication method is not applicable in the traditional sense, as it was a quantitative comparison using Lin's CCC against a predicate.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study explicitly designed to show how much human readers improve with AI vs. without AI assistance was not described in the provided text. The evaluation focused on the performance of the software (nordicBOLD and nordicDSC) in comparison to predicate devices, and usability, not on human reader performance with AI assistance. The device does not incorporate AI algorithms based on neural networks, limiting the scope for typical AI-assisted MRMC studies.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done

Yes, from the descriptions of the nordicBOLD and nordicDSC evaluations, they appear to be standalone performance assessments of the algorithms' output compared to predicate devices. The "results from nordicBOLD were evaluated in comparison with equivalent results from the predicate device" and "results from the nordicDSC were confirmed to be the same as the predicate" indicates an algorithm-only evaluation, followed by expert review for nordicBOLD.

7. The Type of Ground Truth Used

• nordicBOLD: The ground truth appears to be established by comparison to the results generated by the predicate device (nordicBrainEx) and reviewed by "internal and external clinical experts." This suggests a comparative ground truth based on established clinical performance of a cleared device, with expert consensus on equivalency.
• nordicDSC: The ground truth for comparison was the output of the predicate device (nordicDSC K212720), with equivalency quantified by Lin's Concordance Correlation Coefficient.

8. The Sample Size for the Training Set

The document does not specify the sample size for the training set. It states that the device does not incorporate "AI algorithms based on neural networks," implying that traditional machine learning (which often requires training data) or deep learning was not the primary methodology. The device uses "rule-based algorithms for the calculation of metrics from dynamic MRI data."

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

Given that the device "does not incorporate AI algorithms based on neural networks" and uses "rule-based algorithms," the concept of a "training set" with established ground truth as typically understood for deep learning models is likely not applicable. The algorithms are rule-based, meaning their performance depends on the pre-defined rules, which are likely derived from scientific principles, clinical knowledge, and established methodologies for fMRI and DSC analysis, rather than learned from a labeled training dataset.

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