Abstract: According to an embodiment of the present disclosure, there is provided a method of predicting a brain tissue lesion distribution, the method including: a model learning operation of learning a prediction model for predicting a brain tissue lesion distribution of a subject by using brain image data of a plurality of previous patients; an input obtaining operation of obtaining input data from brain image data of the subject; and an output operation of generating output image data including information on the lesion distribution after recanalization treatment for the subject, by using the prediction model. The prediction model includes a success prediction model that is learned by using data of patients in which recanalization treatment is successful among the plurality of previous patients, and a failure prediction model that is learned by using data of patients in which recanalization treatment fails among the plurality of previous patients.

Abstract: A method for collateral circulation assessment, using dynamic MRI images, includes the steps of: receiving dynamic perfusion MRI images of a subject by an analysis device; extracting arterial phase images, capillary phase images, and venous phase images from the dynamic perfusion MRI images by the analysis device; inputting the arterial phase images, capillary phase images, and venous phase images into pre-trained, deep learning models, respectively, to calculate a plurality of collateral circulation prediction probability values by the analysis device; and conducting a final collateral circulation assessment for the subject, using the plurality of collateral circulation prediction probability values by the analysis device.

Abstract: According to an embodiment of the present disclosure, there is provided a method of predicting a brain tissue lesion distribution, the method including: a model learning operation of learning a prediction model for predicting a brain tissue lesion distribution of a subject by using brain image data of a plurality of previous patients; an input obtaining operation of obtaining input data from brain image data of the subject; and an output operation of generating output image data including information on the lesion distribution after recanalization treatment for the subject, by using the prediction model. The prediction model includes a success prediction model that is learned by using data of patients in which recanalization treatment is successful among the plurality of previous patients, and a failure prediction model that is learned by using data of patients in which recanalization treatment fails among the plurality of previous patients.