2025· International Journal of Computer Theory and Engineering· 0 citations· 34 references
TL;DR
Combining GLCM texture analysis with ML provides a potential way of improving the precision and reliability of MV disease diagnosis, ultimately supporting efforts to enhance public health and early disease diagnosis, in alignment with global healthcare initiatives.
Abstract
Mitral Valve (MV) pathologies such as Mitral Valve Prolapses (MVP), Mitral Stenosis (MS), and type III regurgitation should be diagnosed as early as possible for the better management of the patients. This research work presents a method for the classification of MV diseases using echocardiographic images and texture analysis of the images using Gray Level Co-occurrence Matrix (GLCM) features in conjunction with Machine Learning (ML) classifiers. Initially, a Convolutional Neural Network (CNN) was employed to categorize echocardiographic images into two standard views: Apical Four-Chamber (A4C) and Parasternal Long-Axis (PLA). Next, the energy, contrast, correlation, and the entropy of GLCM-based texture features were obtained. The features were then fed into ML classifiers such as Random Forest (RF), Neural Networks (NN), Ensemble models to classify MV conditions. In the A4C view, the Neural Network Classifier (NNC) obtained an accuracy of 85% while in the Parasternal Long Axis (PLA) view, the accuracy was 84%. Some of the features of GLCM that were deemed important in the performance of the model were revealed. The results show that combining GLCM texture analysis with ML provides a potential way of improving the precision and reliability of MV disease diagnosis. The findings of this study contribute to advancements in cardiovascular disease detection by integrating machine learning techniques with echocardiographic analysis, ultimately supporting efforts to enhance public health and early disease diagnosis, in alignment with global healthcare initiatives.
MitralVision reliably distinguishes clinically significant MR using single-view B-mode echocardiography without Doppler input for model inference and may support more standardized MR screening.
R. Sandler, J. Sokol, S.G. Pawar et al.· Journal of the American Soci...· 0 citations
The framework integrating MG-APSO-DnCNN and GIN enables accurate and robust ApHCM subtype classification, supporting cardiologists in early diagnosis, patient risk stratification, and treatment planning.
P. Venkatesan, A. Rajeswari, N. R. Shanker· Current medical imaging· 0 citations
A deep learning-based method for automatically classifying heart conditions from echocardiography data using the EfficientNetB0 architecture, which has the potential to improve cardiovascular disease prognosis and early detection, thereby increasing the scalability of sophisticated diagnostic capabilities in a variety of healthcare settings.
Taha Tahseen, Afshan Fatima· International Journal of Eng...· 0 citations
Abstract Aims Early identification of obstructive coronary artery disease (ObCAD) is crucial because it is strongly associated with acute myocardial infarction. We developed a weighted average ensemble model integrating deep learning (DL) and machine learning (ML) to leverage imaging and clinical data for enhancing the detection of ObCAD. Methods and results A retrospective cohort of 1054 patients was used to develop an ensemble model combining a 3D Vision Transformer with eXtreme Gradient Boosting and CatBoost for binary classification of ObCAD (>50% stenosis). Unstructured data comprised 3D cardiac non-contrast computed tomography (CT) scans, while structured data included 11 demographic and clinical features. Obstructive coronary artery disease labels were derived from corresponding coronary CT angiography. Model performance was evaluated using 10-fold cross-validation with fold-wise Wilcoxon signed-rank testing. The ensemble model achieved a mean receiver operating characteristic area under the curve (ROC AUC) of 0.81 ± 0.04 and an accuracy of 0.76 ± 0.04. It demonstrated a statistically significantly higher ROC AUC than individual component models. Feature importance analysis identified age, chest pain, and sex as the most influential predictors of ObCAD. Gradient-weighted class activation mapping visualization indicated that the 3D Vision Transformer primarily focused on cardiac regions containing coronary artery calcium deposits. Conclusion Integrating DL-based imaging analysis with ML-based clinical modelling enhances the discriminative performance for ObCAD detection with complementary interpretability. This ensemble framework demonstrates potential to support clinical decision-making by identifying high-risk patients using routine cardiac CT combined with patient-level clinical data. Future studies using external validation and coronary artery calcium scores may further improve risk prediction.
Doyoung Park, Lin-Xuan Yan, Arman Ahmad Khan et al.· European Heart Journal - Dig...· 0 citations
The proposed anatomically guided BackMix augmentation combined with semi-supervised ensemble learning can improve classification accuracy, robustness, and interpretability in echocardiographic analysis under limited annotation conditions, offering a promising approach for automated AS assessment across independent clinical datasets.
Fatima Ezzahra Elkouahy, Badreddine Labakoum, H. Ouahid et al.· Journal of Electronics Elect...· 0 citations
This study presents a fully ultrasound-based and cost-effective approach for the automatic grading of aortic valve calcification, which plays a critical role in the assessment of aortic stenosis. To eliminate radiation exposure associated with computed tomography, the proposed method relies exclusively on echocardiographic images and was trained on a dedicated dataset constructed for this study. A Vision Transformer (ViT) model operating on ROI frames extracted from the aortic valve region was developed to classify four calcification levels: mild, moderate, severe, and critical. During testing, the model achieved 85% accuracy and a macro-averaged precision of 0.74, demonstrating a reliable decision mechanism with a low false-positive tendency. To further evaluate the architectural choice, ImageNet pre¬trained ResNet50 and EfficientNet-B3 models were trained and tested under identical conditions. Although CNN-based architectures produced competitive results, the ViT model demonstrated more balanced performance, particularly in intermediate and advanced grades, and achieved the highest macro-averaged metrics among the evaluated models. By providing a radiation-free, reproducible, and economically accessible solution, the proposed framework offers a clinically applicable decision-support system for the evaluation of aortic stenosis.
M. Çakır, Murat Ekinci, Elif Baykal Kablan et al.· Ömer Halisdemir Üniversitesi...· 0 citations
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