2025· International Journal of Computer Theory and Engineering· Vol 17, pp. 212-224· 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
INTRODUCTION
Accurate classification of apical hypertrophic cardiomyopathy (ApHCM) subtypes is challenging due to morphological variability and overlapping phenotypes. Conventional echocardiography provides limited visualization of the apex. Artifacts induced during left ventricular opacification (LVO) complicate diagnostic interpretation. A deep learning-based framework enhances image quality and improves subtype classification.
MATERIALS AND METHODS
In this work, a deep learning-based framework is used for ApHCM subtype classification. Apical four-chamber end-diastolic frames from 3,200 individual patients were extracted from the EchoNet-Dynamic Dataset. Two cardiology experts manually annotated images into pure ApHCM, relative ApHCM, mixed ApHCM, and normal classes, based on apical wall thickness and morphological characteristics, using a computer vision annotation tool. A deep learning pipeline integrated multilevel graph-based adaptive particle swarm optimization with a deep denoised convolutional neural network (MG-APSO-DnCNN) to suppress reverberation and clutter artifacts from LVO echocardiograms. Enhanced images were then segmented using a U-Net-based levelset model to delineate the left ventricular (LV) apex. Morphological and LV wall features were extracted from the segmented region, and a graph isomorphism network (GIN) was trained to capture both local hypertrophic patterns and global ventricular morphology for subtype classification. The framework was designed to distinguish among pure ApHCM, relative ApHCM, and mixed ApHCM.
RESULTS
The framework achieved a classification accuracy of 96.2%, with a precision, recall, and F1-score of approximately 95%. Cross-validation results indicate stable performance (95.8% ± 0.4%, p < 0.001), and ablation experiments confirmed the contribution of each pipeline component.
DISCUSSION
By combining denoising, segmentation, and graph-based learning, the framework addressed limitations caused by LVO artifacts and improved the recognition of subtle ApHCM subtypes. These results demonstrate the clinical potential of integrating morphological feature extraction with deep learning in echocardiography.
CONCLUSION
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
One of the primary causes of death worldwide is still heart disease. Although echocardiography is a commonly used method for identifying cardiovascular diseases, precise interpretation of echocardiogram pictures necessitates specialist medical knowledge. In order to overcome this difficulty, this paper presents a deep learning-based method for automatically classifying heart conditions from echocardiography data using the EfficientNetB0 architecture. For medical picture analysis, EfficientNetB0 offers a lightweight yet effective solution thanks to its compound scaling technique, which balances network depth, width, and resolution. In order to lessen the need for human interpretation, the model is trained to automatically extract intricate and distinctive features from echocardiographic images. EfficientNetB0 is especially well-suited for real-time clinical use since it guarantees great accuracy at a cheap computing cost by utilizing its efficiency and good generalization potential. This strategy seeks to assist healthcare providers in enhancing diagnostic accessibility, consistency, and efficiency. The suggested approach 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
Aortic stenosis (AS) is a prevalent and progressive valvular heart disease requiring accurate severity assessment for optimal clinical decision-making. Transthoracic echocardiography (TTE) is the standard diagnostic modality; however, its interpretation remains operator-dependent and subject to inter-observer variability. In this study, we propose an anatomically guided BackMix-enhanced semi-supervised learning framework for automated echocardiographic view classification and AS severity assessment. The approach leverages Gradient-weighted Class Activation Mapping (Grad-CAM) to preserve diagnostically relevant anatomical regions during augmentation while modifying background areas to mitigate shortcut learning. A semi-supervised self-training strategy combined with an ensemble classification framework was used to exploit both labeled and unlabeled data. The framework was evaluated on the TMED2 dataset of echocardiography, comprising 24,964 TTE images. To assess generalizability, external validation was performed on an independent dataset of 300 cardiologist-labeled echocardiographic images, including apical four-chamber (A4C), parasternal long-axis (PLAX), and parasternal short-axis (PSAX) views. Experimental results demonstrated that the proposed method outperformed the baseline semi-supervised model, improving view classification accuracy by approximately 3% and AS severity classification accuracy by 4–6%, with the largest gain observed in moderate AS. Performance remained consistent on the external validation dataset, supporting the robustness of the proposed approach. Statistical analysis confirmed the significance of these improvements (p < 0.01). Grad-CAM evaluation further demonstrated improved localization of clinically relevant regions. These findings suggest that 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
