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.

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.