Abstract: A deep learning-based stent prediction method including: setting as a region of interest, a region in which a procedure is to be performed among blood vessel regions of a target patient, and obtaining a first intravascular ultrasound (IVUS) image, which is a preprocedural IVUS image of the region of interest, obtaining a plurality of first IVUS cross-sectional images into which the first IVUS image is divided at predetermined intervals, extracting feature information about procedure information of the target patient, obtaining mask image information in which a blood vessel boundary and an inner wall boundary are distinguished from each other, with respect to the plurality of first IVUS cross-sectional images, and predicting progress of a stent procedure containing a postprocedural area of a stent for the target patient, by inputting, into an artificial intelligence model, the plurality of first IVUS cross-sectional images, the feature information, and the mask image information.

Abstract: A method of analyzing a plaque tissue component based on deep learning, the method including: extracting a plurality of first intravascular ultrasound (IVUS) cross-sectional images into which a first IVUS image that is a preprocedural IVUS image of a patient is divided at predetermined intervals; labeling each of the plurality of first IVUS cross-sectional images by using label indices corresponding to plaque tissue components to form labeled images, performing image conversion to obtain a polar coordinate image through which a distribution of tissue components for each angle is identifiable by performing a coordinate transformation based on the labeled images, extracting a label vector for each angle based on the polar coordinate image, and outputting output data obtained by quantifying the tissue components for each angle by using an artificial intelligence model that is trained by using, as training data, the label vector for each angle.

Abstract: A method for calculating a coronary artery calcium score, the method comprising acquiring a target image for a coronary artery and myocardium before contrast enhancement, identifying the coronary artery included in the target image by using an artificial neural network, calculating a coronary artery calcium score based on the identified coronary artery, wherein the artificial neural network is trained based on a training database generated via alignment between a pre-acquired image of a coronary artery and myocardium before contrast enhancement and a pre-acquired image of a coronary artery and myocardium after contrast enhancement.

Abstract: A medical image learning method of a medical image process apparatus includes preparing a plurality of body X-ray images for learning as an input of a learning data set, preparing internal diagnostic indicator information corresponding to each of the plurality of body X-ray images for learning as a label of the learning data set, and learning an artificial neural network model using the learning data set. The internal diagnostic indicator information includes information on a cardiovascular border. The information on the cardiovascular border includes information on at least one of an aortic knob, a pulmonary conus, a left atrial appendage, an upper right cardiac border, a lower right cardiac border, a lower left cardiac border, a descending aorta, a carina, an upper end point of a diaphragm, a right pulmonary artery, a posterior cardiac border, and an anterior spinal border.

Abstract: Provided are a diagnostic device and a diagnostic method for predicting fractional flow reserve (FFR) and diagnosing coronary artery lesions via a coronary angiography-based machine learning algorithm. A deep learning-based diagnostic method for diagnosing an ischemic lesion includes: obtaining an angiography image of a patient's blood vessel; extracting a region of interest (ROI) from the angiography image; acquiring diameter information of the blood vessel in the ROI; extracting morphological features of the blood vessel based on the diameter information; and obtaining a predictive FFR value by inputting the morphological features to an artificial intelligence (AI) model and determining whether a lesion is an ischemic lesion.

Abstract: A method for determining the size of a bicuspid annulus of bicuspid, including acquiring an image of the heart including the left ventricle and the aorta; generating a first plane, which includes a line that passes through two base points in the bicuspid of the image of the heart; and generating multiple second planes, which are obtained per each rotation, while rotating the first plane multiple times by a predetermined angle about the line that passes through the two base points; measuring the cross-sectional area of each of at least one of the left ventricle and the aorta, which are formed on the first plane and the multiple second planes; selecting a plane for measuring the size of a bicuspid annulus among the first plane and the multiple second planes based on the measured cross-sectional area; and measuring the size of the bicuspid annulus based on the selected plane.

Abstract: Provided are a diagnostic system for predicting fractional flow reserve (FFR) through a machine learning algorithm based on an ultrasound image of a coronary artery and diagnosing the presence of a coronary artery lesion, and a diagnostic method thereof. The diagnostic method of diagnosing an ischemic lesion of a coronary artery includes: obtaining an intravascular ultrasound (IVUS) image of a coronary artery lesion of a patient; obtaining a mask image in which a vascular lumen is separated, by inputting the IVUS image into a first artificial intelligence model; extracting an IVUS feature from the mask image; and obtaining an FFR prediction value by inputting information including the IVUS feature into a second artificial intelligence model, and determining presence of an ischemic lesion.

Abstract: The present invention relates to an image processing system for a catheter operation. One embodiment comprises: a storage unit for at least temporarily storing a plurality of candidate routes on which a guide wire can proceed on a changing route caused by vascular bifurcations while proceeding from a first point by using a blood vessel image; and a processor for selecting, from an image inputted while an operation proceeds, at least one route corresponding to the current shape of the guide wire on the basis of a similarity comparison between the current shape of the guide wire and each of the plurality of candidate routes.

Abstract: A myocardium image analysis method comprising acquiring a target image including precontrast-enhanced myocardium, based on a type of coronary artery related to the myocardium, distinguishing the myocardium included in the target image, using an artificial neural network, providing information on the distinguished myocardium, wherein the artificial neural network is trained based on a training database generated by matching images for training of precontrast-enhanced coronary artery and myocardium and images for training of postcontrast-enhanced coronary artery and myocardium.

Abstract: A method for calculating a coronary artery calcium score, the method comprising acquiring a target image for a coronary artery and myocardium before contrast enhancement, identifying the coronary artery included in the target image by using an artificial neural network, calculating a coronary artery calcium score based on the identified coronary artery, wherein the artificial neural network is trained based on a training database generated via alignment between a pre-acquired image of a coronary artery and myocardium before contrast enhancement and a pre-acquired image of a coronary artery and myocardium after contrast enhancement.

Abstract: A medical image learning method of a medical image process apparatus includes preparing a plurality of body X-ray images for learning as an input of a learning data set, preparing internal diagnostic indicator information corresponding to each of the plurality of body X-ray images for learning as a label of the learning data set, and learning an artificial neural network model using the learning data set. The internal diagnostic indicator information includes information on a cardiovascular border. The information on the cardiovascular border includes information on at least one of an aortic knob, a pulmonary conus, a left atrial appendage, an upper right cardiac border, a lower right cardiac border, a lower left cardiac border, a descending aorta, a carina, an upper end point of a diaphragm, a right pulmonary artery, a posterior cardiac border, and an anterior spinal border.

Abstract: A method for determining the size of a bicuspid annulus of bicuspid, including acquiring an image of the heart including the left ventricle and the aorta; generating a first plane, which includes a line that passes through two base points in the bicuspid of the image of the heart; and generating multiple second planes, which are obtained per each rotation, while rotating the first plane multiple times by a predetermined angle about the line that passes through the two base points; measuring the cross-sectional area of each of at least one of the left ventricle and the aorta, which are formed on the first plane and the multiple second planes; selecting a plane for measuring the size of a bicuspid annulus among the first plane and the multiple second planes based on the measured cross-sectional area; and measuring the size of the bicuspid annulus based on the selected plane.

Abstract: Provided are a diagnostic device and a diagnostic method for predicting fractional flow reserve (FFR) and diagnosing coronary artery lesions via a coronary angiography-based machine learning algorithm. A deep learning-based diagnostic method for diagnosing an ischemic lesion includes: obtaining an angiography image of a patient's blood vessel; extracting a region of interest (ROI) from the angiography image; acquiring diameter information of the blood vessel in the ROI; extracting morphological features of the blood vessel based on the diameter information; and obtaining a predictive FFR value by inputting the morphological features to an artificial intelligence (AI) model and determining whether a lesion is an ischemic lesion.

Abstract: Disclosed is a method for measuring trabecular bone parameters from MRI images, including: scanning an experimental group with a 3D MRI scanner; segmenting the MRI images to extract bone area and perform skeletonization of the bone area; detecting end-point, joint and branch voxels in the skeleton to analyze bone structure; and measuring trabecular bone parameters based on the result of the structural analysis. The method enables diagnosing osteoporosis.