Automated detection of lung conditions for monitoring thoracic patients undergoing external beam radiation therapy

A computer-implemented method for radiation therapy support, the method comprising:

• receiving, during a therapy delivery phase, an input image comprising a 2D projection image acquired by an imaging apparatus, wherein the imaging apparatus is part of a radiation therapy delivery device, the input image representing at least a region of interest of a patient and acquired before delivery of a dose fraction, among multiple dose fractions associated with a treatment plan for the patient, by the radiation therapy delivery device;
• processing, during the therapy delivery phase, to detect a medical condition including a side effect caused by radiation therapy treatment, the processing based on a machine learning model pre-trained on training imagery, the processing comprising:
• applying the input image to an input layer, the input layer comprising a matrix of nodes of a neural network, wherein a node of the matrix of nodes corresponds with a pixel of the input image;
• and
• calculating an output vector, during the therapy delivery phase, the output vector including accumulated responses, wherein an index of entries of the output vector corresponds with a specified class, among a group of classes each representing a medical condition including a side effect of radiation therapy, wherein the specified class represents an indication of a presence of a respective medical condition;
• providing the output vector including the indication of a presence of the respective medical condition, and providing during the therapy delivery phase, based on the output vector, an indication for one or more clinical actions to be performed in relation to the patient, wherein at least one of the one or more clinical actions includes an adaptation of the treatment plan.