AI-based system could help triage brain MRIs
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An artificial intelligence-driven system that automatically combs through brain MRIs for abnormalities could speed care to those who need it most, according to a study published in Radiology: Artificial Intelligence.

In this retrospective study, a deep learning approach using T2-weighted fluid-attenuated inversion recovery (FLAIR) images was developed to classify brain MRI as “likely normal” or “likely abnormal.” A convolutional neural network model was trained on a large heterogeneous dataset collected from two different continents and covering a broad panel of pathologies including neoplasms, hemorrhage, infarcts, and others.

Three datasets were used. Dataset A consisted of 2839 patients, Dataset B consisted of 6442 patients, and Dataset C consisted of 1489 patients and was only used for testing. Datasets A and B were split into training, validation, and test sets. A total of three models were trained: Model A, Model B, and Model A+B. All three models were tested on subsets from Dataset A, Dataset B, and Dataset C separately. The evaluation was performed using annotations based on the images as well as labels based on the radiologic reports.

Model A trained on Dataset A from one institution and tested on Dataset C from another institution reached an F1-score of 0.72 and an area under the curve of 0.78 when compared with findings from the radiologic reports.

In particular, the model shows relatively good performance to differentiate likely normal or likely abnormal brain examinations using data from different institutions.