Neural network-based coronary dominance classification of RCA angiograms

Background. Cardiac dominance classification is essential for SYNTAX score estimation, which is a tool used to determine the complexity of coronary artery disease and guide patient selection toward optimal revascularization strategy. Objectives. Cardiac dominance classification algorithm based on the analysis of right coronary artery (RCA) angiograms using neural network Method. We employed convolutional neural network ConvNext and Swin transformer for 2D image (frames) classification, along with a majority vote for cardio angiographic view classification. An auxiliary network was also used to detect irrelevant images which were then excluded from the data set. Our data set consisted of 828 angiographic studies, 192 of them being patients with left dominance. Results. 5-fold cross validation gave the following dominance classification metrics (p=95%): macro recall=93.1%, accuracy=93.5%, macro F1=89.2%. The most common case in which the model regularly failed was RCA occlusion, as it requires utilization of LCA information. Another cause for false prediction is a small diameter combined with poor quality cardio angiographic view. In such cases, cardiac dominance classification can be complex and may require discussion among specialists to reach an accurate conclusion. Conclusion. The use of machine learning approaches to classify cardiac dominance based on RCA alone has been shown to be successful with satisfactory accuracy. However, for higher accuracy, it is necessary to utilize LCA information in the case of an occluded RCA and detect cases where there is high uncertainty

Three-dimensional reconstruction of the pelvic bones on MRI scans

BACKGROUND: Pelvimetry is an important part of the obstetric examination for predicting a mismatch between the size of the fetus and the mothers pelvis, which leads to difficulty or impossibility of vaginal delivery. Contracted pelvis is one of the main causes of maternal birth trauma and perinatal morbidity and mortality. AIM: To create a computer vision model for automatic segmentation and three-dimensional (3D) reconstruction of the pelvic bones. METHODS: A 3D U-Net-based neural network was used and trained on T2 weighted images in frontal projection (repetition time, 7500; echo time, 130; slice thickness, 4mm; field-of-view, 4039; matrix, 256256). The sample size covered 49 patients. The training and test samples included 42 and 7 examinations, respectively. The segmentation of areas of interest was done manually and verified by a specialist. The sample size was justified by achieving representativeness of the data for obtaining a qualitative model (according to the SorensenDice coefficient). RESULTS: 3D reconstructions of the pelvic bones were obtained. The average Sorensen-Dice coefficient on the accuracy of pelvic bone segmentation in the test sample was 0.86. The result justified the use of a 3D U-Net-based neural network as a tool capable of perceiving a 3D structure of images and conducting qualitative segmentation. The results allow further work on automating the determination of key points at reconstructions. CONCLUSIONS: A computer vision model for automatic segmentation of the pelvic bones to obtain 3D reconstruction of images was created. This enabled the next stage of the study, i.e. the development of a model for determining the key points in the images and the distances between the points

Optimized calculator for a qualitative risk assessment of osteoporotic fractures for the population of Moscow

BACKGROUND: The FRAX tool (a 10-year fracture risk assessment) is recommended to diagnose osteoporosis and optimize the number of patients who need to undergo X-ray densitometry. Due to various circumstances, the integration of a full-fledged FRAX tool into the digital circuits of the Moscow City Health Department is problematic. AIM: The study aimed to develop a calculator of the 10-year probability of osteoporotic fractures to optimize the routing of patients for examination. METHODS: An optimized Half-FRAX calculator was created based on the FRAX tool from the University of Sheffield, which was developed using the results of population studies of the Russian Federation. All data used in the original FRAX algorithm, i.e. sex, age, height, weight, and T-criterion (if available) and other important parameters such as a history of fractures, parental hip fractures, smoking, rheumatoid arthritis, secondary osteoporosis, and glucocorticoid and alcohol intake were included in the risk assessment calculator. An algorithm for interaction with the FRAX website was developed and implemented to verify critical levels of patient stratification by multiple consecutive enumerations of different combinations of body mass index (BMI) measurements (0.1 discretization) and age (1-year discretization). Data from clinical guidelines were taken as thresholds. RESULTS: When implementing the developed algorithm by modeling various combinations of BMI, T-criterion, and risk factors (RF), the absence of RFs and BMI 25 (upper limit of normal) in women was shown to guarantee the exclusion from the orange zone where densitometry should be performed. In men, BMI was not a RF. If a RF was present, a patient was recommended to consult a doctor. If no T-criterion was present, but a RF was detected, the patient was indicated for densitometry. Similar results were reported for women with the same indices. In the absence of the RF and with a T-criterion 2.5, low fracture risk factor was indicated for both men and women. CONCLUSIONS: An optimized Half-FRAX calculator for the 10-year probability of major osteoporotic fractures was developed, which may optimize the routing of patients for densitometry and reduce the burden on radiology departments in Moscow. This will allow patients to be timely referred to the clinical specialists for consultations. Half-FRAX is integrated into the Osteoporosis Digital Platform (https://telemedai.ru/cifrovaya-platforma-osteoporoz/half-frax)