Abstract: A method for monitoring valve health. The method may include receiving valve data from a source, pre-processing the received valve data, and detecting at least one event based on the pre-processed valve data to provide event signature data. The method may also include determining that the detected event is an anomaly based on the event signature data, classifying the anomaly based on the event signature data, and performing an action based on the classified anomaly. The method may also include training an event classification model to classify the anomaly according to the event signature data. Training the event classification model to classify the anomaly may include reviewing and labeling a plurality of plots of the event signature data by a subject matter expert and then providing the labeled event signature data to the event classification model, and validating the classification model by the subject matter expert.

Abstract: Systems and techniques for generating and/or employing a medical imaging stroke model are presented. In one example, a system employs a convolutional neural network to generate output data regarding a brain anatomical region based on diffusion-weighted imaging (DWI) data associated with the brain anatomical region and apparent diffusion coefficient (ADC) data associated with the brain anatomical region. The system also detects presence or absence of a medical stroke condition associated with the brain anatomical region based on the output data.

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.

Abstract: Systems and techniques for generating and/or employing a medical imaging stroke model are presented. In one example, a system employs a convolutional neural network to generate output data regarding a brain anatomical region based on diffusion-weighted imaging (DWI) data associated with the brain anatomical region and apparent diffusion coefficient (ADC) data associated with the brain anatomical region. The system also detects presence or absence of a medical stroke condition associated with the brain anatomical region based on the output data.