Abstract: Provided is a medical image analysis method including acquiring a target medical image, detecting a target joint spacing region from the target medical image, acquiring a first value related to a width of a joint part from the target medical image, acquiring a second value related to joint spacing from the target joint spacing region, and calculating a target joint condition indicator indicating a joint condition based on the first value and the second value.

Abstract: The present disclosure relates to an image analysis method, system, and computer program. The image analysis method of the present disclosure includes: receiving a query image; extracting one or more regions of interest from the query image; calculating a first feature for each of the regions of interest by respectively applying the regions of interest to one or more ROI (region of interest) feature extraction models independently learned in order to extract features of the regions of interest; and calculating analysis values of the query image by applying the first features of the regions of interest to a pre-learned integration analysis model. According to the present disclosure, it is possible to reduce the influence on an analysis model by an error that training data created for map learning of an entire image may have, and it is also possible to increase learning accuracy and objectivity of a deep neural network.