Жарияланымдар / Публикации
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Тип записи:Публикация, Hybrid deep neural networks for automated detection of coronary pathologies in angiographic video sequences(2025-12-30) ;Oleksandr Poplavskyi ;Iryna A. Mezhiievska ;Waldemar Wójcik; Sergii V. PavlovCoronary artery disease (CAD) remains one of the most prevalent causes of death worldwide, demanding accurate and timely diagnosis to guide treatment. Coronary angiography is the clinical reference standard for visualizing arterial patency; however, its manual interpretation is labour-intensive and subject to considerable inter-observer variability. This study introduces a hybrid deep learning framework that fuses convolutional neural networks with long short -term memory units to exploit both spatial and temporal cues in cine angiography. After automated frame extraction, selection and normalization, sequences are processed end-to-end to detect the presence and type of clinically significant coronary pathology. Validation on an expert-labeled video dataset demonstrates robust performance (accuracy=0.88; precision=0.85; recall=0.92; F1-score=0.88; AUC≈0.91). Gradient-weighted class activation mapping highlights lesion-relevant vessel segments, supporting model interpretability. These findings confirm that the proposed CNN-LSTM architecture can reduce observer variability and accelerate decision-making by delivering consistent, automated assessment of angiographic studies. - Некоторые метрики заблокированы вашиминастройками согласия
Тип записи:Публикация, Prediction of Percutaneous Coronary Intervention from Clinical and ECG Data Using Machine Learning: A Retrospective Single-Center Observational Study(Multidisciplinary Digital Publishing Institute, 2026-05-06); ;Chingiz A. Alimbayev ;Kassymbek Ozhikenov ;Kairat KaribayevAiman K. OzhikenovaThe aim of this study was to evaluate the feasibility of predicting percutaneous coronary intervention (PCI) based on clinical, laboratory, and electrocardiographic data available at various stages of hospitalization. A retrospective single-center study was conducted, including 137 patients with suspected coronary artery disease. The fact that PCI was performed during the current hospitalization was considered as the endpoint. Taking into account the temporary availability of data, three sets of signs were formed: basic (SAFE), including indicators available at admission; clinical (CLINICAL); and extended (EXTENDED), supplemented with glycemic parameters. Logistic regression, random forest, and gradient boosting were used to build the models. The assessment was carried out using repeated stratified cross-validation (5 × 10). The main metrics were ROC-AUC, PR-AUC, accuracy and F1-measure. The models demonstrated moderate predictive ability. The basic model (SAFE) showed a ROC-AUC of 0.734 ± 0.092, while the best results were achieved using an extended model based on a random forest (ROC-AUC 0.755 ± 0.079). The addition of glycemic parameters provided a moderate improvement in prediction quality. In the logistic regression, the most significant predictor was the presence of type 2 diabetes mellitus (OR = 7.36; p < 0.001). The results indicate the potential for using non-invasive data to assess the likelihood of PCI in the early stages of hospitalization. However, the models show moderate accuracy and require further validation on larger and more independent samples.
