Aortography Keypoint Tracking for Transcatheter Aortic Valve Implantation Based on Multi-Task Learning.

Currently, transcatheter aortic valve implantation (TAVI) represents the most efficient treatment option for patients with aortic stenosis, yet its clinical outcomes largely depend on the accuracy of valve positioning that is frequently complicated when routine imaging modalities are applied. Therefore, existing limitations of perioperative imaging underscore the need for the development of novel visual assistance systems enabling accurate procedures. In this paper, we propose an original multi-task learning-based algorithm for tracking the location of anatomical landmarks and labeling critical keypoints on both aortic valve and delivery system during TAVI. In order to optimize the speed and precision of labeling, we designed nine neural networks and then tested them to predict 11 keypoints of interest. These models were based on a variety of neural network architectures, namely MobileNet V2, ResNet V2, Inception V3, Inception ResNet V2 and EfficientNet B5. During training and validation, ResNet V2 and MobileNet V2 architectures showed the best prediction accuracy/time ratio, predicting keypoint labels and coordinates with 97/96% accuracy and 4.7/5.6% mean absolute error, respectively. Our study provides evidence that neural networks with these architectures are capable to perform real-time predictions of aortic valve and delivery system location, thereby contributing to the proper valve positioning during TAVI.

Real-time coronary artery stenosis detection based on modern neural networks.

Invasive coronary angiography remains the gold standard for diagnosing coronary artery disease, which may be complicated by both, patient-specific anatomy and image quality. Deep learning techniques aimed at detecting coronary artery stenoses may facilitate the diagnosis. However, previous studies have failed to achieve superior accuracy and performance for real-time labeling. Our study is aimed at confirming the feasibility of real-time coronary artery stenosis detection using deep learning methods. To reach this goal we trained and tested eight promising detectors based on different neural network architectures (MobileNet, ResNet-50, ResNet-101, Inception ResNet, NASNet) using clinical angiography data of 100 patients. Three neural networks have demonstrated superior results. The network based on Faster-RCNN Inception ResNet V2 is the most accurate and it achieved the mean Average Precision of 0.95, F1-score 0.96 and the slowest prediction rate of 3 fps on the validation subset. The relatively lightweight SSD MobileNet V2 network proved itself as the fastest one with a low mAP of 0.83, F1-score of 0.80 and a mean prediction rate of 38 fps. The model based on RFCN ResNet-101 V2 has demonstrated an optimal accuracy-to-speed ratio. Its mAP makes up 0.94, F1-score 0.96 while the prediction speed is 10 fps. The resultant performance-accuracy balance of the modern neural networks has confirmed the feasibility of real-time coronary artery stenosis detection supporting the decision-making process of the Heart Team interpreting coronary angiography findings.

CYP2C19 polymorphism frequency in Russian patients in Central Russia and Siberia with acute coronary syndrome.

PURPOSE: The aim of this study is to investigate the frequency of CYP2C19*2, *3 allelic variants, associated with poor response to clopidogrel, and CYP2C19*17, associated with excessive response to clopidogrel, in patients with acute coronary syndrome (ACS) from Siberia and Moscow regions of Russia. PATIENTS AND METHODS: The study included 512 ACS patients who were subsequently treated with coronary arterial stenting. The subjects assigned were from the cities of Central (Novosibirsk, Kemerovo), Eastern (Irkutsk), Northern (Surgut) Siberia regions and from Moscow region. The mean age of patients enrolled was 63.9±10.9 years. Among the assigned subjects, the proportion of men accounted for 80% and women 20%. RESULTS: According to the results obtained in the present study, from 16% up to 27.5% of patients in different regions of Russia have at least one CYP2C19 "poor metabolizer" (PM) allele variant affecting clopidogrel metabolism and, therefore, suppressing its antiplatelet activity. CYP2C19*17 allele variant was identified with the frequency of 15.4% up to 33.3%. The study revealed the presence of statistically significant differences in CYP2C19*3 allele frequency between the Russian ethnic group patients from Eastern and Central Siberia (p=0.001; odds ratio=1.05 [95% confidence interval 1.01-1.09]). CONCLUSION: The study revealed statistically significant differences between the allele frequencies in Eastern and Central Siberia, which can probably be caused by a considerable number of Buryats inhabiting Eastern Siberia.