Self-supervised deep learning model for coronary artery segmentation from invasive X-ray coronary angiography (ICA), designed to reduce reliance on large annotated datasets. CM-UNet combines a Contrastive Masked Autoencoder (CMAE) with a UNet backbone: an online encoder-decoder branch reconstructs masked image patches while a momentum branch produces contrastive embeddings, jointly pretraining the network on unannotated angiography images before fine-tuning on a small labeled set. Fine-tuning with only 18 annotated images (instead of 500) led to just a 15.2% drop in Dice score, versus a 46.5% drop for baseline models trained without this self-supervised pretraining -- demonstrating strong label efficiency for coronary segmentation.

Domain-Shuffle Temporal Attention Network for coronary vessel extraction from X-ray coronary angiography (XCA), trained entirely on synthetic temporal XCA data without requiring manual vessel annotations. By leveraging synthetic data generation and a domain-shuffle temporal attention mechanism, DOSTA-Net avoids the need for costly expert-labeled real angiography sequences while still learning temporally consistent vessel segmentation across frames of an XCA sequence.

Machine-learning surrogate model for estimating pulsatile hemodynamic fields (velocity, pressure) in coronary arteries from a steady-state computational fluid dynamics (CFD) prior, avoiding the high computational cost of full pulsatile CFD. The model, a neural field conditioned on hemodynamic boundary conditions, is discretisation-independent and can be parametrised with message-passing or self-attention layers by relaxing point-wise action to permutation-equivariance. Evaluated on 74 stenotic coronary arteries from coronary CT angiography (CCTA) with patient-specific pulsatile CFD as ground truth, the model produced accurate, discretisation-independent estimates of pulsatile velocity and pressure fields.

Transformer-based multi-view multiple-instance learning (MIL) framework for patient-level coronary stenosis classification from multi-view invasive coronary angiography. Rather than requiring expensive view-level stenosis annotations, SegmentMIL is trained end-to-end on real-world clinical data using only patient-level labels already present in hospital systems, and jointly predicts stenosis presence while localizing the affected artery (left/right) and segment. It captures temporal dynamics and dependencies across the multiple angiographic views per patient (which prior view-level models ignore), and outperforms both single-view models and classical MIL baselines on internal and external clinical evaluations.

Automatic coronary artery segmentation pipeline for coronary CT angiography (CCTA). A 2D DenseNet classifier first screens out CT slices that don't contain coronary artery, then a 3D-UNet -- enhanced with dense blocks in the encoder for richer feature extraction and residual, feature-rectifying blocks in the decoder -- segments the coronary artery tree in the remaining slices. A Gaussian-weighted merging scheme combines overlapping 3D patch predictions, up-weighting the more reliable predictions near each patch's center. On the authors' in-house CCTA dataset, the method achieved a Dice similarity coefficient of 0.826.

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).

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.

Coronary artery calcium (CAC) scoring model that transfers a CNN trained for calcium scoring on non-contrast CT (NCCT) to coronary CT angiography (CCTA), where iodinated contrast otherwise confounds calcium detection and large annotated CCTA training sets are scarce. The CAC-scoring CNN is split into a feature generator and a classifier; the feature generator is trained on the NCCT source domain and adapted to the CCTA target domain via adversarial learning combined with a maximum-mean-discrepancy loss, while the source-domain classifier is reused unchanged for the target domain. Builds directly on the authors' earlier non-contrast CT calcium-scoring network.

Two-stage convolutional neural network that automatically detects and anatomically labels coronary artery, thoracic aorta, and cardiac-valve calcifications in low-dose chest CT acquired for lung-cancer screening. A first CNN with a large receptive field (via dilated convolutions) identifies and anatomically labels candidate calcifications; a second CNN filters true positives from the candidates. Trained and evaluated on 1,744 CT scans from the National Lung Screening Trial (NLST), reaching an F1 of 0.89 (soft-filter reconstructions) / 0.84 (sharp-filter reconstructions) for coronary artery calcifications and a linearly-weighted kappa of 0.90-0.91 for per-subject cardiovascular risk categorization versus the manual reference standard.

Deep learning platform for fully automatic segmentation and phenotyping of coronary intravascular ultrasound (IVUS) pullbacks, packaged with a desktop GUI and CLI. A convolutional encoder-decoder network delineates the internal (lumen) and external elastic lamina borders on each cross-sectional IVUS frame; downstream rule-based analysis derives lumen area, plaque area, plaque burden, automatically flags lesions with plaque burden exceeding 40%, and reports minimum lumen area and maximum plaque burden along the pullback. Also supports end-diastolic gating and manual contour editing. Trained on 305 clinical IVUS pullbacks (270 train / 35 validation) from Philips and Boston Scientific catheters at Emory University; downstream evaluations have applied DeepIVUS to tasks such as automated detection of stent underexpansion.

Self-supervised model that performs single-frame digital-subtraction-angiography-style vessel/background separation directly from a single live (non-subtracted) coronary angiogram frame, then supports fine-tuned coronary vessel segmentation. A U-Net-style network is pretrained via an image-to-image translation objective on 58,128 unannotated angiography DICOM series (3,756 patients), then fine-tuned for vessel segmentation on just 40 expert-annotated frames, reaching a Dice of 0.828 on the held-out fine-tuning set and a new state-of-the-art Dice of 0.755 on the public XCAD benchmark. Intended to help clinicians visualize potential stenosis sites without requiring true two-frame digital subtraction acquisition.

Clinically-informed modification of the ResNet-18 architecture for identifying occlusion myocardial infarction (OMI) -- a severe, often ST-elevation-negative heart attack caused by complete blockage of a coronary artery -- from a single 12-lead ECG. The network first learns lead-specific temporal features via 1xk temporal convolutions, then learns cross-lead spatial concordance/discordance (e.g. reciprocal ST changes) via a 12x1 spatial convolution placed after the residual blocks, with saliency maps highlighting the most relevant leads and waveform regions for explainability. Benchmarked against ResNet-18 and other CNN/random-forest baselines on a multisite real-world clinical dataset of 10,893 ECGs (OMI rate 6.5%), reaching a test AUROC of 0.889 and an average precision of 0.587, outperforming the compared models.

nnU-Net-based segmentation network that detects and delineates stenotic lesions directly from X-ray coronary angiography frames, developed for the ARCADE (MICCAI 2023) stenosis-detection challenge. A companion model (YOLO-Angio, same team) handles vessel-tree segmentation; StenUNet focuses specifically on pixel-wise localization of stenotic regions. Placed 3rd overall among ARCADE challenge entrants with an F1 score of 0.5348 on the hold-out test set, within 0.0005 of the 2nd-place team.

U-Net-variant segmentation model that identifies and quantifies coronary artery calcium (CAC) directly from routine non-gated, non-contrast chest CT scans -- the kind ordered for lung-cancer screening or unrelated indications rather than a dedicated cardiac scan -- so that the tens of millions of such scans performed annually can be opportunistically screened for cardiovascular risk without any extra imaging. Predicted calcium masks are combined with the CT's Hounsfield units to compute an Agatston-equivalent score. Trained on 446 expert-segmented scans from 98 medical centers across the U.S. Department of Veterans Affairs national health system (capturing substantial heterogeneity in scanners and protocols) and benchmarked against 795 patients with a paired same-year gated CAC study: nongated AI-CAC differentiates zero-vs-nonzero and <100-vs->=100 Agatston categories with 89.4% (F1 0.93) and 87.3% (F1 0.89) accuracy respectively, and its score stratifies 10-year all-cause mortality (CAC 0 vs. >400: 25.4% vs. 60.2%, hazard ratio 3.49) and composite stroke/MI/death risk (33.5% vs. 63.8%, hazard ratio 3.00). In a simulated opportunistic-screening run across 8,052 low-dose CT scans, cardiologists confirmed 99.2% of patients flagged with AI-CAC >400 would benefit from lipid-lowering therapy. Code and trained model weights are both public under an MIT license.

Fully automated pipeline for interpreting coronary angiograms that chains four purpose-built neural networks: (1) angiographic projection-angle identification, (2) left/right coronary artery detection, (3) arterial segment localization, and (4) stenosis-severity estimation. Trained on 13,843 angiographic studies (195,195 videos) from 11,972 adult patients at UCSF (2008-2019), with projection-angle and LCA/RCA-detection tasks each reaching precision/sensitivity/F1 at or above 90%. For predicting obstructive coronary artery disease (>=70% stenosis), CathAI reaches an AUC of 0.862 internally, 0.869 on external angiograms from the University of Ottawa Heart Institute, and 0.775 after retraining on quantitative-coronary-angiography labels from the Montreal Heart Institute core lab. No public code or model weights have been released.

Multi-view foundation model for coronary angiography trained with video-text contrastive learning on 203,808 angiography videos from 28,117 patients across 32,473 studies at the Montreal Heart Institute, externally validated on 4,249 studies from UCSF. Integrates multiple angiographic projections with attention-based pooling for study-level assessment spanning diagnostic, prognostic, and disease-progression tasks: significant-stenosis detection (AUROC 0.888 internal / 0.89 external), stenosis-percentage estimation (MAE 13.6% vs. 19.0% for clinical reports), chronic total occlusion, intracoronary thrombus, and coronary calcification detection. Transfer learning further enables one-year MACE prediction (AUROC 0.79) and LVEF estimation (MAE 7.3%) from the same angiography embeddings, with a mean in-hospital inference time of 4.2 seconds.

AI-driven pipeline for quantitative coronary-stenosis assessment from routine DICOM coronary angiography videos, combining vessel tracking with a video Swin3D transformer trained and validated on 182,418 angiography videos spanning 5 years at the Montreal Heart Institute. Achieves a mean absolute error of 20.15% and a classification AUROC of 0.8294 for stenosis-percentage prediction against cardiologist assessment, with lower inter-rater variability than two expert interventional cardiologists, and can be fine-tuned to quantitative coronary angiography (QCA) data for even lower error (MAE 7.75%).

Largest federated cardiac CT analysis to date (n=8,104 scans) across a real-world federation of German university hospitals, addressing partially-labeled data across sites via a two-step semi-supervised knowledge-distillation strategy: task-specific CNNs first predict on unlabeled data per label type, then a SWIN-UNETR transformer learns from these predictions with label-specific heads. Learns a single federated model that simultaneously predicts TAVI-relevant landmarks (aortic hinge points, coronary ostia, membranous septum) and calcification from cardiac CT, improving generalizability over UNet-based baselines on downstream tasks.

Multi-instance-learning (MIL) model for detecting >=50% coronary stenosis directly from curved multiplanar reformation (CMR) images generated during routine coronary CT angiography (CCTA) reads, without requiring slice-level annotations. A multi-range Hounsfield-unit preprocessing pipeline (Sobel edge detection across five attenuation windows) highlights plaque and vessel-wall structures, which a VGG16-based encoder with positional encoding and multi-head attention aggregates across each patient's 'bag' of up to 36 CMR slices per artery to give an interpretable, attention-weighted patient-level prediction. Trained and five-fold cross-validated on 900 real-world CCTA cases (776 LAD / 694 RCA / 600 LCX) from Sahlgrenska University Hospital, reaching AUCs of 0.91-0.92 across the three major coronary arteries. Code (preprocessing + MIL training pipeline) is public; the clinical CMR dataset and trained weights are not released.

Multimodal cardiac MRI foundation model that fuses 3D+T cine CMR (short-axis and long-axis views) with tabular patient health records (demographics, metabolic, and lifestyle factors) from 42,000 UK Biobank participants. Two-stage self-supervised pretraining -- masked-image reconstruction, then imaging-tabular contrastive alignment -- produces representations that transfer to whole-heart segmentation, cardiac phenotype/physiological-feature regression, and cardiac/metabolic disease classification within one unified framework.

Open-source, reproducible pipeline for representing 12-lead ECGs as explicit graphs -- nodes per lead-timepatch, with edges encoding established inter-lead spatial relationships (fully-connected limb- and chest-lead subgraphs bridged via leads I, aVF, V4, and V5) -- and classifying them with a Graph Convolutional Network, paired with GNNExplainer to surface which leads and lead-pairs drove each prediction. Evaluated on PTB-XL for five-class diagnostic superclass classification (AUC 0.86) and, with the same architecture, on anteroseptal-vs-inferior myocardial-infarction localization (AUC 0.92), externally validated on the population-based SHIP cohort (AUC 0.87). Explainability analysis showed the GNN's lead attention recovers standard ECG diagnostic criteria (e.g. V1-V3 for anteroseptal MI, II/III/aVF for inferior MI). Developed at University Medical Center Gottingen; code and an example trained checkpoint are released under CC BY-NC 4.0.

Vision-language model that jointly embeds a cardiac MRI study, treated as video, with the impression section of its clinical report. Combines a video encoder over cine/LGE frame sequences with a Bio+ClinicalBERT text encoder using CLIP-style contrastive training. Supports zero-shot and few-shot classification of cardiomyopathies, amyloidosis, and LV dysfunction, plus image/report retrieval and structured report drafting. Trained on a private, single-institution corpus of roughly 11,000-14,000 CMR study-report pairs from Cleveland Clinic and Case Western.

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.

Segments coronary vessels from invasive X-ray angiography images and automatically quantifies the degree of stenosis along the extracted centerlines. Combines MedSAM, a Segment-Anything-style vision model, with a Mamba-based VM-UNet segmentation branch for efficient long-range feature modeling. Trained and evaluated on the ARCADE, DCA1, and GH angiography datasets by researchers at Ocean University of China and Shandong University.