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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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The fMRI Hardware System is a stimulus presentation and response collection system intended to be used by trained professionals to facilitate auditory and visual stimulation to be used in functional MR Imaging (fMRI) based on BOLD contrast.

nordicAktiva is a stimulus presentation software intended to be used by trained professionals to facilitate auditory and visual stimulation to be used in functional MR maging (fMRI)based on BOLD contrast. In addition, it records responses from the NordicNeuroLab ResponseGrips.

The device is indicated for use during BOLD fMRI examinations of individuals over 2 years of age. It is not intended for the diagnosis of disease or for the direct treatment of conditions.

nordicAktiva would be used in an MRI department of a hospital or clinic, when a functional MRI exam based on BOLD contrast is required involving the provision of visual or auditory stimulation to a subject inside the MRI scanner.

The fMRI Hardware System is a stimulus presentation and response collection system intended to be used by trained professionals to facilitate auditory and visual stimulation to be used in functional MR Imaging (fMRI) based on BOLD contrast.

nordicAktiva is a stimulus presentation software intended to be used by trained professionals to facilitate auditory and visual stimulation to be used in functional MR imaging (fMRI)based on BOLD contrast. In addition, it records responses from the NordicNeuroLab ResponseGrips.

The device is indicated for use during BOLD fMRI examinations of individuals over 2 years of age. It is not intended for the diagnosis of disease or for the direct treatment of conditions.

nordicAktiva would be used in an MRI department of a hospital or clinic, when a functional MRI exam based on BOLD contrast is required involving the provision of visual or auditory stimulation to a subject inside the MRI scanner.

The system presents auditory and visual stimulus to the patient gives feedback through a pair of handheld grips. A synchronizes the stimulus presentation software with the MR scanner. The System consists of five subsystems: AudioSystem, ResponseGrip, SyncBox and nordicAktiva.

The system is used for fMRI studies. fMRI stands for functional Magnetic Resonance Imaging. This technique is primarily used for determining which area of the brain is responsible for specific processes controlling essential functions such as movement, speech, hearing, and vision. This information can form part of the pre-operative planning process in patients with brain tumors, for example. Functional MRI can also be used for research into specific neurological conditions or investigating cognitive function and networks in general.

The System is used to present the stimulus necessary to provoke physiological processes in the brain. Visual [VisualSystem] and auditory [AudioSystem] stimulus and manual responses from the patient [ResponseGrips] are of primary interest. The timing of the visual and auditory stimulation is critical to make sure that the correct MR image of the activated brain is linked to the stimulus presented. A synchronization unit [SyncBox], connected between the MR-scanner and the stimulus presentation software [nordicAktiva], is included in the system to make sure that the synchronization is correct.

The provided text does NOT describe acceptance criteria for a device that involves performance metrics such as accuracy, sensitivity, or specificity. Instead, the document is a 510(k) premarket notification for an fMRI Hardware System where the primary goal is to demonstrate substantial equivalence to a legally marketed predicate device.

The "acceptance criteria" discussed in the document relate to compliance with regulatory standards and software verification, not clinical performance metrics of the device in diagnosing or aiding in diagnosis. The device, nordicAktiva, is a stimulus presentation and response collection system for fMRI, and it is explicitly stated that "It is not intended for the diagnosis of disease or for the direct treatment of conditions."

Therefore, the requested information regarding a table of acceptance criteria and reported device performance (in the sense of diagnostic accuracy), sample size for test sets, expert involvement in ground truth, MRMC studies, and standalone performance is not applicable to this submission, as the device's function does not involve such diagnostic performance evaluations.

The document states that a new version of the software, nordicAktiva, was updated, and the testing performed was focused on verifying that the modified device fulfills its defined characteristics and requirements, maintaining substantial equivalence to its predicate.

Here's a breakdown of what the document does provide regarding "acceptance criteria" and "study":

1. A table of acceptance criteria and the reported device performance

The document does not provide a table with "acceptance criteria" in terms of diagnostic performance (e.g., sensitivity, specificity, AUC) and corresponding reported device performance values. The closest information is a comparison of technological characteristics between the modified device and the predicate in Section 1.4.1. The "acceptance criteria" for this submission are more aligned with regulatory compliance and functional verification.

From Section 1.5.1 and 1.5.2, the acceptance criteria are implicit in meeting:

• International and FDA-recognized consensus standards (ISO 14971, IEC 62304, IEC 62366-1).
• Design and performance specifications, as well as user needs, when operated according to instructions.
• Intended use, technological characteristics claims, requirement specifications, and risk management results.

The "reported device performance" is the conclusion that the device "demonstrates compliance" with these standards and "meets the acceptance criteria and is adequate for its intended use and specifications."

2. Sample size used for the test set and the data provenance

The document does not specify a "test set" in the context of a clinical performance study with patient data. The testing mentioned (Section 1.5.1) is "non-clinical performance testing" and "verification and validation tests have been performed to address intended use, the technological characteristics claims, requirement specifications and the risk management results." This implies engineering and software testing rather than a clinical study with a patient sample size. Therefore, sample size and data provenance in that context are not relevant or provided.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. The type of testing performed (non-clinical performance, verification, and validation) would typically involve qualified engineers and testers, but not "experts" to establish "ground truth" for diagnostic performance, as the device is not intended for diagnosis.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable. This is typically used in clinical studies for establishing ground truth, which is not the nature of the testing described.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

No. An MRMC study is not mentioned. The device is a stimulus presentation and response collection system, not an AI-assisted diagnostic tool.

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

Not applicable. The device's function is to facilitate fMRI by presenting stimuli and collecting responses, not to operate as a standalone diagnostic algorithm.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)

Not applicable. "Ground truth" in the diagnostic sense is not relevant for the type of device and testing described. The "truth" for this device would be its correct functioning according to its design specifications (e.g., stimulus presented correctly, response recorded accurately, synchronization maintained).

8. The sample size for the training set

Not applicable. There is no mention of a training set for a machine learning or AI model, as this is not an AI-based diagnostic device.

9. How the ground truth for the training set was established

Not applicable. As there is no training set, this information is not provided.

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nordicDSC is a medical image post-processing software for the dynamic time course of the dynamic susceptibility contrast (DSC) enhanced MR data of the human brain to be used by trained professionals such as physicians, radiologists, and medical technicians to yield information useful in clinical applications.

The software is intended to be used off-line and in combination with a DICOM handling platform that can allow for user interactions, sending, receiving, and viewing of data, data quality control, and connection to network.

nordicDSC is an image analysis module that can be integrated into hospital infrastructure to analyze dynamic susceptibility contrast-enhanced (DSC) perfusion data obtained from Magnetic Resonance Imaging (MRI). The product can be deployed as a plugin to a DICOM handling platform.

The program functionality can be divided into three main categories:

1. Accessing image data
2. MR image processing and analysis
3. Generating output, including information regarding blood flow, time to peak, and leakage.

The NordicNeuroLab AS nordicDSC Software underwent non-clinical performance testing to demonstrate its compliance with established standards and its suitability for its intended use. However, the provided document does not contain a detailed study proving the device meets specific acceptance criteria in terms of performance metrics. Instead, it primarily focuses on comparing the nordicDSC software with a predicate device (nordicICE Software, K090546) and verifying its compliance with general regulatory and software development standards.

Here's an analysis of the requested information based on the provided text:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly present a table of specific quantitative acceptance criteria alongside corresponding performance metrics for the nordicDSC device. It states, "The test results in this 510(k)premarket notification demonstrates that nordicDSC: ... Meets the acceptance criteria and is adequate for its intended use and specifications." This is a general statement of compliance, not a report of specific performance against defined criteria.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document provides no information regarding the sample size used for any test set or the provenance of the data (e.g., country of origin, retrospective or prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

The document does not mention the number of experts used to establish ground truth for any test set, nor does it specify their qualifications.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

The document does not describe any adjudication method used for a test set.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study or any assessment of human reader improvement with or without AI assistance.

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

The document does not explicitly state that a standalone performance study (algorithm only without human-in-the-loop) was conducted. While it refers to "non-clinical performance testing" and "verification and validation tests," these are primarily framed around compliance to standards and comparison to a predicate device's features rather than a direct, quantitative standalone performance evaluation against a gold standard.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)

The document does not specify the type of ground truth used for any testing.

8. The sample size for the training set

The document does not mention any training set or its sample size. The primary focus is on performance testing and comparison to a predicate, not on a machine learning model training process that would typically involve a "training set."

9. How the ground truth for the training set was established

Since no training set is mentioned, there is no information on how its ground truth was established.

Summary of what the document does provide regarding testing:

• Non-clinical performance testing: "Non-clinical performance testing has been performed on the nordicDSC Software and demonstrates compliance with the following International and FDA-recognized consensus standards and FDA guidance document: ISO 14971, IEC 62304, IEC 62366-1, IEC 82304-1 Edition 1.0 2016-10."
• Verification and Validation: "Verification and Validation tests have been performed to address intended use, the technological characteristics claims, requirement specifications and the risk management results."
• Nondeterministic algorithm variation: "The algorithm is inherently nondeterministic, so head-to-head comparisons have to consider the internal variation, which has been found to be within +/- 10%." This provides a very general statement about the algorithm's consistency but not its accuracy or specific performance against a clinical gold standard.
• Substantial Equivalence: The primary method of demonstrating safety and effectiveness appears to be through showing substantial equivalence to a predicate device (nordicICE Software, K090546) based on intended use, indications for use, and technical/operational characteristics.

Conclusion:

The provided 510(k) summary focuses on demonstrating substantial equivalence to a predicate device and compliance with general software and risk management standards. It explicitly states that "The test results in this 510(k)premarket notification demonstrates that nordicDSC: ... Meets the acceptance criteria and is adequate for its intended use and specifications." This implies that acceptance criteria were defined and met. However, the document does not disclose the specific quantitative acceptance criteria, the detailed study design (including sample sizes, data provenance, ground truth establishment, or expert involvement), or the specific performance results that would allow for a complete answer to your request. The "Brief discussion of the nonclinical tests" refers to general compliance rather than specific clinical performance metrics.

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The fMRI Hardware System is a stimulus presentation and response collection system intended to be used by trained professionals to facilitate auditory and visual stimulation to be used in functional MR Imaging (fMRI) based on BOLD contrast.

The VisualSystem allows video signals from the stimulus presentation PC to enter the shielded scanner room and to be presented to the patient through a set of coil-mounted displays [VisualSystem HD] or by an in-room LCD monitor.

The system presents auditory and visual stimulus to the patient gives feedback through a pair of handheld grips. A synchronization module synchronizes the stimulus presentation software with the MR scanner. The System consists of five subsystems: AudioSystem, VisualSystem, ResponseGrip, SyncBox and nordicAktiva.

The system is used for fMRI studies. fMRI stands for functional Magnetic Resonance Imaging. This technique is useful when determining certain diseases, gaining more information about a patient's condition or investigating cognitive functions. The technique is also used for examining the area of the brain affected in patients suffering from a brain tumor in both the pre-operative and post-operative stages.

The System is used to present the stimulus necessary to provoke physiological processes in the brain. Visual [VisualSystem] and auditory [AudioSystem] stimulus and manual responses from the patient [ResponseGrips] are of primary interest. The timing of the visual and audio stimulation is critical to make sure that the correct MR image of the brains activity is linked to the stimulus presented. A synchronization unit [SyncBox], connected between the MR-scanner and the stimulus presentation software [nordicAktiva], is included in the system to make sure that the synchronization is correct.

The provided text is a 510(k) summary for a medical device (fMRI Hardware System, specifically an updated VisualSystem HD component) and does not contain the kind of detailed information typically found in a study proving a device meets acceptance criteria for an AI/ML-based diagnostic device.

This document describes a hardware system for presenting stimuli in fMRI studies, and the changes involve technical upgrades (e.g., higher resolution display, HDMI instead of VGA, integrated eye-tracking vs. separate unit). The testing discussed is primarily functional, biocompatibility, and electrical safety, consistent with a hardware device.

Therefore, I cannot extract the requested information from this document because it does not pertain to the performance of an AI/ML algorithm or diagnostic accuracy. The questions about AI model training, ground truth establishment, expert adjudication, MRMC studies, and standalone algorithm performance are not relevant to the described device and the information provided.

The document does contain information about:

• Device Name: fMRI Hardware System (specifically, the VisualSystem HD component).
• Intended Use: "A stimulus presentation and response collection system intended to be used by trained professionals to facilitate auditory and visual stimulation to be used in functional MR Imaging (fMRI) based on BOLD contrast." (page 4)
• Testing Summary: Functional tests, biocompatibility tests (for a rubber eye guard), and electrical safety/EMC tests. (page 7)
• Sample Size for testing: Not explicitly stated as a "sample size" in the context of diagnostic accuracy, but functional, biocompatibility, and safety tests would have involved specific units or batches of the device.

To answer your specific questions, this document does not provide:

1. A table of acceptance criteria and reported device performance related to diagnostic accuracy or AI/ML. The "performance" mentioned is functional, biocompatibility, and safety.
2. Sample sizes for a "test set" in an AI/ML context, nor data provenance.
3. Number of experts or their qualifications for establishing ground truth for a test set.
4. Adjudication method for a test set.
5. Information about a multi-reader multi-case (MRMC) comparative effectiveness study or AI assistance effect size.
6. Results of a standalone (algorithm-only) performance study.
7. The type of ground truth used (expert consensus, pathology, outcomes data).
8. Sample size for a training set.
9. How ground truth for a training set was established.

This is a regulatory submission for a hardware component upgrade, not an AI/ML diagnostic software.

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nordicBrainEx provides analysis and visualization capabilities of dynamic MRI data of the brain, presenting the derived properties and parameters in a clinically useful context.

The nordicBrainEx is a post-processing application for dynamic MRI data developed with focus on ease of use and high performance on a standard Windows workstation. The software provides comprehensive functionality for dynamic image analysis and visualization of MRI data, where signal changes over time are analyzed to determine various modality dependent functional parameters. The following algorithms provide the main functional analyses of the application.

• . BOLD: BOLD fMRI analysis is used to highlight small magnetic susceptibility changes in the human brain in areas with altered blood-flow resulting from neuronal activity.
• . DTI: Diffusion analysis is used to visualize local water diffusion properties from the analysis of diffusion-weighted MRI data. Fiber tracking utilizes the directional dependency of the diffusion to display the white matter structure in the brain.
• . DSC: Calculations of perfusion related parameters that provide information about the blood vessel structure and characteristics. Examples of such maps are blood volume, blood flow, time to peak, mean transit time and leakage.
• DCE Perfusion analysis: Enables analysis of Dynamic Contrast Enhancement (DCE) MR data. In contrast to DSC Perfusion which uses T2* weighted sequence, DCE Perfusion uses T1-weighted sequence to measure bolus passage. Parameters are calculated to provide information about blood vessel structure and characteristics. The output maps from these calculations include interstitial volume (Ve), plasma volume (Vp), transfer constant map (Ktras), rate constant map (Kep), area under the curve (AUC), time to peak (TTP), peak, wash In and wash Out.
  In addition to these specific functional analyses, the application also provides general visualization tools, a database for data handling, and a reporting feature. This is to ensure that the workflow of the application is optimized to ensure efficiency and high throughput in a clinical environment.

The provided text does not contain specific acceptance criteria, reported device performance metrics, or details of a study that proves the device meets acceptance criteria in the format requested.

The document is a 510(k) premarket notification letter and summary for the nordicBrainEx device, indicating its substantial equivalence to a predicate device (nordicICE). While it mentions "extensive in-house testing" and "prospectively defined verification and validation activities" to assure the device meets design and performance specifications, it does not provide the quantitative results or the methodology of these tests in detail.

Therefore, I cannot extract the information required for the table and bullet points provided in your request. The document explicitly states that the "successful completion of said tests verifies the claimed characteristics of nordicBrainEx, and thus supports the determination of substantial equivalence," but it does not present the 'acceptance criteria' or the 'reported device performance' in a measurable way that can be tabulated.

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