# Enhanced x-ray angiography analysis and interpretation using deep learning

> **NIH NIH R44** · VIGILANT MEDICAL, INC. · 2020 · $702,450

## Abstract

Enhanced x-ray angiography analysis and
interpretation using deep learning
Over 1 Million diagnostic X-ray angiograms are performed annually in the US to guide treatment of coronary
artery disease (CAD) and cost over $12 billion. Despite being the clinical standard of care, visual interpretation
is prone to inter- and intra-observer variability. Recently as part of the NHLBI supported Prospective Multicenter
Imaging Study for Evaluation of Chest Pain (PROMISE) trial, our research team showed that cardiologists
misinterpreted over 19% of angiograms obstructive CAD (greater than 50% vessel stenosis). Given the centrality
of angiographic interpretation to the development of a treatment plan, reduced accuracy can lead to unnecessary
poor outcomes and increased costs to our healthcare system. The potential impact is significant given that
increasing interpretation accuracy by 1% could positively benefit over 10,000 patients each year in the US alone.
Thus, our team is developing an X-ray angiographic analysis system (DeepAngio) driven by deep learning
technology to enhance physician interpretation. In Phase I, the PROMISE dataset of over 1,000 angiograms was
used to build our Convolutional Neural Network (CNN) based deep learning model. We achieved a 0.89 Area
Under the Receiving Operating Characteristic (AUROC) for identifying obstructive CAD in images with expert
scored ground truth (exceeding our proposed Phase I milestone of >0.85 AUROC).
Now in Phase II, we present an innovative image learning pipeline to incorporate anatomical and spatiotemporal
information from video sequences (similar to a cardiologist reader). A full end to end X-ray angiography video
processing pipeline will be developed and tested in a new cohort of 10,000 patient angiograms with normal and
graded abnormal CAD. Our patch-based frame analysis model will advance to CNN full frame-based
classification of angiographic views (left heart vs. right heart) and segmentation of coronary vessels (LAD, LCx,
and RCA). A multiple frame analysis approach enabled by a Recursive Neural Network (RNN) will equip our
model with dynamic temporal information to estimate lesion presence accurately. Our goal for Phase II is to
improve reading specificity and translate our Phase I proof of concept research findings into a clinically
meaningful tool. A multi-reader, multi-case evaluation by a group of interventional cardiologists interpreting with
and without DeepAngio predictions will assess clinical usability to improve coronary stenosis estimation.
In the long term, we hope the combination of a cardiologist with DeepAngio as an assistive tool will improve the
clinical accuracy of angiographic interpretation.

## Key facts

- **NIH application ID:** 10000961
- **Project number:** 5R44HL140794-03
- **Recipient organization:** VIGILANT MEDICAL, INC.
- **Principal Investigator:** Ricardo Henao Giraldo
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $702,450
- **Award type:** 5
- **Project period:** 2018-09-07 → 2023-07-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10000961

## Citation

> US National Institutes of Health, RePORTER application 10000961, Enhanced x-ray angiography analysis and interpretation using deep learning (5R44HL140794-03). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10000961. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*