Open-source, user-guided deep learning tool for coronary artery segmentation from invasive coronary angiography (ICA), designed to improve on traditional quantitative coronary angiography (QCA) edge-detection algorithms that typically require manual correction. Rather than segmenting the whole coronary tree indiscriminately, AngioPy lets the user click a handful of ground-truth points along a specific target vessel (including side branches), and predicts a binary mask for that single artery at the chosen cardiac-cycle time-step. Evaluated against an established QCA system on angiograms from the FAME 2 trial, AngioPy achieved an average F1 score of 0.927 (internal) and 0.924 (external validation), with vessel-diameter and lesion minimal-lumen-diameter measurements showing excellent agreement with QCA (r=0.93-0.96).

Ensemble classifier combining hand-engineered expert features with a deep convolutional neural network for classifying cardiac rhythm from a single-lead ECG recording into normal sinus rhythm, atrial fibrillation, another rhythm, or too noisy to classify. A large set of expert features (from time-, frequency-, and template-based analysis) is fed into a gradient-boosted tree classifier (AdaBoost), and its output is combined with a separate deep CNN operating directly on the raw waveform; combining both feature families measurably outperformed either alone. ENCASE won 1st place in the PhysioNet/Computing in Cardiology Challenge 2017 (single-lead AF classification) with an overall F1 score of 0.83 on the official hidden test set, and remains a widely cited example of combining classical signal-processing features with deep representations for ECG classification.

Automated pipeline that segments both heart ventricles and tracks their motion throughout the cardiac cycle from short-axis cine cardiac MRI, producing 3D bi-ventricular models with per-vertex wall-thickness and curvature measurements over time. Built on a shape-refined multi-task fully convolutional network, followed by non-rigid registration and mesh-based motion tracking. Trained on roughly 400 manually annotated pulmonary hypertension patients as part of Imperial College London's UK Digital Heart Project, and underlies the related 4Dsurvival cardiac-motion survival-prediction study.

Multi-task model that jointly assesses signal quality and detects atrial fibrillation from wrist-worn wearable photoplethysmography (PPG), pretrained on roughly one million simulated unlabeled signals before fine-tuning on labeled wearable data. Uses a 1D CNN with separate output heads for signal quality and arrhythmia detection. Developed by Stanford's Ashley Lab.

Fully automatic pipeline that localizes the heart, segments coronary calcium, and produces an Agatston-style coronary artery calcium score from gated and non-gated chest/cardiac CT. A three-stage 3D CNN performs each step in sequence. Validated across the Framingham, NLST, PROMISE, and ROMICAT-II cohorts, where the resulting calcium score predicted cardiovascular events with hazard ratios up to 4.3. Developed by the Harvard AIM Lab.

Multi-task 12-lead ECG model with output heads for incident atrial-fibrillation risk (as a survival curve), incident mortality risk, prevalent AF classification, sex classification, and age regression. Built on a 1D CNN over the raw waveform, and developed by the Broad Institute's ML4H group as a successor to their ECG-AI model published in Circulation. Trained on ECGs from UK Biobank and Massachusetts General Hospital, neither of which is publicly released.

Multimodal ECG model that pairs a 1D ConvNeXtV2 signal encoder with a BioLinkBERT text encoder, trained with a joint contrastive-and-captioning objective using LLM-generated descriptions of ECG demographics and waveform patterns in place of raw clinical reports. Validated on arrhythmia diagnosis and ECG-based subject identification, reaching an AUROC of 0.938 fine-tuned and 0.812 zero-shot on PTB-XL diagnostic classification. Developed at Rice University.

Self-supervised ECG representation learned purely from patient identity: the model is trained so that ECGs from the same patient, recorded at different times, map to nearby points in latent space, with no other labels required. Linear models trained on these representations showed a 51% average performance gain over training from scratch across sex classification, age regression, LVH detection, and AF detection. Developed by the Broad Institute's ML4H group and trained on 3.2 million private ECGs from Massachusetts General Hospital; 12-lead, lead-I-only, and lead-II-only checkpoints are all released.

Reference benchmark suite for the PTB-XL ECG dataset, providing pretrained xresnet1d, InceptionTime, LSTM, and CPC-pretrained models for 71-label, diagnostic, sub-diagnostic, and super-diagnostic classification of ECG findings. Widely used as a standardized baseline for comparing new ECG classification methods. Developed by Strodthoff et al. at Fraunhofer HHI.