Abstract: A method for identifying the presence or progression of hypoxic ischemic brain injury includes, for each subset of one or more subsets of a three-dimensional medical image of a head of a patient: (i) inputting said each subset into a machine-learning model, (ii) extracting one or more features or feature maps from the machine-learning model, and (iii) constructing, based on the one or more features or feature maps, one of a sequence of vectors. The sequence of vectors is then pooled to obtain a scan-level vector that is used to obtain a score indicating HIBI presence or progression in the patient. For example, the scan-level vector can be inputted into a pre-trained classifier that generates the score based on the scan-level vector. The machine-learning model may be a pre-trained conventional neural network or support vector machine.

Abstract: A method for identifying the presence or progression of hypoxic ischemic brain injury includes, for each subset of one or more subsets of a three-dimensional medical image of a head of a patient: (i) inputting said each subset into a machine-learning model, (ii) extracting one or more features or feature maps from the machine-learning model, and (iii) constructing, based on the one or more features or feature maps, one of a sequence of vectors. The sequence of vectors is then pooled to obtain a scan-level vector that is used to obtain a score indicating HIBI presence or progression in the patient. For example, the scan-level vector can be inputted into a pre-trained classifier that generates the score based on the scan-level vector. The machine-learning model may be a pre-trained conventional neural network or support vector machine.