Abstract: Embodiments relate to a method for assisting the diagnosis of cardioembolic stroke, in which the method is performed by a processor and is to assist the diagnosis of cardioembolic stroke by using chest radiographic images, the method comprising the steps of: acquiring chest radiographic images to be classified of a subject; and acquiring cardioembolic stroke diagnosis auxiliary information from the chest radiographic images to be classified of the subject, by using a neural network model, wherein the neural network model is configured to extract features from the chest radiographic images to be classified of the subject, and determine, based on the extracted features, whether the subject belongs to a group indicative of having cardioembolic stroke.

Abstract: The present invention relates to a sleep apnea diagnostic auxiliary system using a simple skull x-ray image, the system comprising: an input unit for receiving a simple skull x-ray image of a target patient; a prediction unit for analyzing the simple skull x-ray image to predict a possibility of the occurrence of sleep apnea of the target patient; an information providing unit for generating and providing diagnostic auxiliary information on the basis of the possibility of the occurrence of sleep apnea of the target patient; and an artificial intelligence learning model configured to train the prediction unit by using learning data including simple skull x-ray images of a plurality of patients and sleep apnea diagnosis results of the patients.