Abstract: An ischemic stroke mimic is detected, or otherwise predicted, based on medical images acquired from a subject. Medical image data, which include medical images acquired from a head of the subject, are accessed with a computer system. A machine learning model (e.g., one or more deep convolutional neural networks) is trained on training data to estimate a probability of an acute intracranial abnormality being depicted in a medical image. Intracranial abnormality prediction data are generated by inputting the medical image data to the machine learning model. The intracranial abnormality prediction data include an intracranial abnormality probability score for each of the medical images in the medical image data. An ischemic stroke mimic classification for the medical image data is generated based on the intracranial abnormality prediction data, and may be displayed to a user with the computer system.

Abstract: Systems and techniques for generating and/or employing a computed tomography (CT) medical imaging intracranial hemorrhage model are presented. In one example, a system employs a convolutional neural network to generate classification output data regarding a brain anatomical region based on computed tomography (CT) data associated with the brain anatomical region. The system also detects presence or absence of a medical intracranial hemorrhage condition in the CT data based on the classification output data. Furthermore, the system determines a subtype of the medical intracranial hemorrhage condition based on the classification output data. The system also generates display data associated with the subtype of the medical intracranial hemorrhage condition in a human-interpretable format.