3D convolutional autoencoder that filters reverberation clutter artifacts from transthoracic echocardiography (TTE) video sequences, improving downstream measurements such as speckle-tracking strain. Built on a 3D U-Net-style encoder-decoder with an input-output skip connection to preserve fine structures and attention-gate modules to focus on cluttered regions, the network was trained on synthetic clutter simulated across six ultrasound vendors and generalized well to real in vivo artifactual sequences, substantially reducing the discrepancy between cluttered and clutter-free strain profiles while running in a fraction of a second per sequence.

Optimized Hierarchical Fused Fuzzy Deep Reinforcement Learning model for predicting non-sinus (higher-risk) cardiac rhythms from PQRST-analyzed 12-lead ECG data. The three-stage approach combines data preprocessing, reinforcement learning, and fuzzy deep learning to classify sinus vs. non-sinus rhythms. Evaluated on a 12-lead ECG dataset of 10,646 patients, OHFFDRL achieved 94% accuracy, an AUC of 0.91, and was interpreted using SHAP, LIME, calibration curves, adversarial vulnerability analysis, and integrated gradients; TAxis (ventricular repolarization movement range) was found to be the most important distinguishing feature.

Disentangled representation learning model for cardiac image analysis that factorises 2D medical images (MRI, CT) into a spatial 'anatomy factor' (a semantically meaningful multi-channel map, produced by a U-Net-style anatomy encoder) and a non-spatial 'modality factor' (a latent vector capturing imaging-specific characteristics). This disentangled representation supports semi-supervised segmentation using only a fraction of labeled images (matching fully supervised performance), multi-task learning (e.g. jointly regressing cardiac indices), multimodal pooling of MRI and CT data, and image-to-image synthesis between modalities via latent-space arithmetic (swapping modality factors). SDNet also demonstrates that its modality factor alone can predict the input imaging modality with high accuracy.

Self-supervised learning (SSL) model that identifies chronically stressed mother-fetus dyads from raw maternal abdominal ECG (aECG), which contains both maternal and fetal cardiac signals. Built on a self-supervised representation-learning approach originally developed for ECG-based emotion recognition, the model is pretrained on public ECG datasets and evaluated on a cohort of pregnant women with chronic stress exposure validated by psychological inventory, maternal hair cortisol, and the fetal stress index (FSI). Using maternal ECG alone with the publicly pretrained model, it detected the chronic-stress-exposure group with AUROC 0.982 and predicted psychological stress score (R2 0.943), FSI (R2 0.946), and maternal hair cortisol (R2 0.931).

Deep channel-attention network for segmenting the full coronary vessel tree from sequential X-ray coronary angiography (XCA) frames, rather than a single static image. An encoder-decoder architecture fuses temporal-spatial feature maps across the XCA sequence via skip connections, then uses channel-attention blocks in the decoder to refine features and separate thin vessel structures from complex, noisy backgrounds; a Dice loss addresses the severe foreground/background class imbalance typical of XCA. The authors report that SVS-net outperforms prior 2D and video-based baselines on both quantitative vessel-segmentation metrics and visual validation.

Fully automated deep learning workflow for characterizing cardiac mechanics from balanced steady-state free-precession (bSSFP) cine cardiac MRI. It decouples two convolutional networks—a segmentation net (CarSON) and a 3D motion-estimation net (CarMEN)—to derive left- and right-ventricular volumes plus global and regional myocardial strain and strain rate without manual tracing. Trained and validated on healthy and cardiovascular-disease subjects and shown to be robust across MRI vendors, with excellent intra-scanner repeatability for strain. Developed at Massachusetts General Hospital and the Harvard-MIT Division of Health Sciences and Technology.