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AI-Rad Companion Brain MR is a post-processing image analysis software that assists clinicians in viewing, analyzing, and evaluating MR brain images.

AI-Rad Companion Brain MR provides the following functionalities:
• Automated segmentation and quantitative analysis of individual brain structures and white matter hyperintensities
• Quantitative comparison of each brain structure with normative data from a healthy population
• Presentation of results for reporting that includes all numerical values as well as visualization of these results

AI-Rad Companion Brain MR runs two distinct and independent algorithms for Brain Morphometry analysis and White Matter Hyperintensities (WMH) segmentation, respectively. In overall, comprises four main algorithmic features:

• Brain Morphometry
• Brain Morphometry follow-up
• White Matter Hyperintensities (WMH)
• White Matter Hyperintensities (WMH) follow-up

The feature for Brain Morphometry is available since the first version of the device (VA2x), while segmentation of White Matter Hyperintensities was added since VA4x and the follow-up analysis for both is available since VA5x. The brain morphometry and brain morphometry follow-up feature have not been modified and remain identical to previous VA5x mainline version.

AI-Rad Companion Brain MR VA60 is an enhancement to the predicate, AI-Rad Companion Brain MR VA50 (K232305). Just as in the predicate, the brain morphometry feature of AI-Rad Companion Brain MR addresses the automatic quantification and visual assessment of the volumetric properties of various brain structures based on T1 MPRAGE datasets. From a predefined list of brain structures (e.g. Hippocampus, Caudate, Left Frontal Gray Matter, etc.) volumetric properties are calculated as absolute and normalized volumes with respect to the total intracranial volume. The normalized values are compared against age-matched mean and standard deviations obtained from a population of healthy reference subjects. The deviation from this reference population can be visualized as 3D overlay map or out-of-range flag next to the quantitative values.

Additionally, identical to the predicate, the white matter hyperintensities feature addresses the automatic quantification and visual assessment of white matter hyperintensities on the basis of T1 MPRAGE and T2 weighted FLAIR datasets. The detected WMH can be visualized as a 3D overlay map and the quantification in count and volume as per 4 brain regions in the report.

Here's a structured overview of the acceptance criteria and study details for the AI-Rad Companion Brain MR, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Reported Device Performance

Study Details

1. Sample Size Used for the Test Set and Data Provenance:

   • White Matter Hyperintensities (WMH) Feature: 100 subjects (Multiple Sclerosis patients (MS), Alzheimer's patients (AD), cognitive impaired (CI), and healthy controls (HC)).
   • White Matter Hyperintensities (WMH) Follow-up Feature: 165 subjects (Multiple Sclerosis patients (MS) and Alzheimer's patients (AD)).
   • Data Provenance: Data acquired from Siemens, GE, and Philips scanners. Testing data had balanced distribution with respect to gender and age of the patient according to target patient population, and field strength (1.5T and 3T). This indicates a retrospective, multi-vendor, multi-national (implied by vendor diversity) dataset.

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

   • Number of Experts: Three radiologists.
   • Qualifications: Not explicitly stated beyond "radiologists." It is not specified if they are board-certified, or their years of experience.

3. Adjudication Method for the Test Set:

   • For each dataset, three sets of ground truth annotations were created manually.
   • Each set was annotated by a disjoint group consisting of an annotator, a reviewer, and a clinical expert.
   • The clinical expert was randomly assigned per case to minimize annotation bias.
   • The clinical expert reviewed and corrected the initial annotation of the changed WMH areas according to a specified annotation protocol. Significant corrections led to re-communication with the annotator and re-review.
   • This suggests a 3+1 Adjudication process, where three initial annotations are reviewed by a clinical expert.

4. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done:

   • No, an MRMC comparative effectiveness study comparing human readers with and without AI assistance was not done. The study focuses on the standalone performance of the AI algorithm against expert ground truth.

5. If a Standalone (i.e. algorithm only without human-in-the loop performance) Was Done:

   • Yes, a standalone performance study was done. The "Accuracy was validated by comparing the results of the device to manual annotated ground truth from three radiologists." This evaluates the algorithm's performance directly.

6. The Type of Ground Truth Used:

   • Expert Consensus / Manual Annotation: The ground truth for both WMH and WMH follow-up features was established through "manual annotated ground truth from three radiologists" and involved a "standard annotation process" with annotators, reviewers, and clinical experts.

7. The Sample Size for the Training Set:

   • The document states that the "training data used for the fine tuning the hyper parameters of WMH follow-up algorithm is independent of the data used to test the white matter hyperintensity algorithm follow up algorithm." However, the specific sample size for the training set is not provided in the given text.

8. How the Ground Truth for the Training Set Was Established:

   • The document implies that the WMH follow-up algorithm "does not include any machine learning/ deep learning component," suggesting a rule-based or conventional image processing algorithm. Therefore, "training" might refer to parameter tuning rather than machine learning model training.
   • For the "fine-tuning the hyper parameters of WMH follow-up algorithm," the ground truth establishment method for this training data is not explicitly detailed in the provided text. It only states that this data was "independent of the data used to test" the algorithm.

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