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).

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.