Abstract: A system (100) for generating a three-dimensional image of a coronary tree (449) includes a memory (151) and a processor (152). The processor (152) is configured to to: obtain a sequence of two-dimensional angiogram images (410) corresponding to a moving heart from a single viewpoint of an imaging device; and generate, using a trained machine learning model (430), a three-dimensional representation (211A) of the coronary tree (449) based on the sequence of the two-dimensional angiogram images (410) and cardiac motion of the moving heart.

Abstract: The present invention relates to maintenance alert monitoring. In order to improve maintenance alert monitoring, there is provided an alert monitoring apparatus (10). The alert monitoring apparatus (10) comprises an input module (12), a change point analyzer (14), a classifier (16), an evolution analyzer (18), and an output module (20). The input module (12) is configured to receive a plurality of time series data of a maintenance alert from a plurality of electronic devices over a period of time, wherein each time series data represents a sequence of the maintenance alert over time from a respective electronic device. The change point analyzer (14) is configured to determine a sequence of alerting volumes over time based on the plurality of time series data of the maintenance alert, and to apply a change point detection method to the sequence of alerting volumes to divide the period of time into a succession of time segments, and to identify temporal boundaries of the time segments as change points.

Abstract: A method and system for modifying a prediction model. In particular, an inaccuracy of the prediction model is categorized into one of at least three categories. Different modifications are made to the prediction model depending on the category of the inaccuracy. In particular examples, an inaccuracy category defines what training data is used to modify the prediction model.