Background: Although artificial intelligence systems that diagnosis among different conditions from medical images are long term aims, specific goals for automation of human-labor, time-consuming tasks are not only feasible but equally important. Acute conditions that require quantitative metrics, such as acute ischemic strokes, can greatly benefit by the consistency, objectiveness, and accessibility of automated radiological reports.
Methods: We used 1,878 annotated clinical brain MRIs from patients with acute ischemic strokes to generate a fully automated artificial intelligence based system that outputs radiological reports in addition to the infarct volume, 3D digital infarct mask, and the feature vector of anatomical regions affected by the acute infarct.
Findings: The performance of our system was comparable to that of an expert evaluator. The weight of the components of the feature vectors that supported the prediction of the reports, as well as the prediction probabilities are outputted, making the pre-trained models behind our system interpretable. The system is publicly available, runs in real time, in local computers, with minimal computational requirements, and it is readily useful for non-expert users.
Interpretation: Our system supports large-scale processing of new and legacy data, enabling clinical and translational research. The generation of reports indicates that our fully automated system is able to extract quantitative, objective, structured, and personalized information of potential clinical relevance from stroke MRIs.