Predicting High Coronary Artery Calcium Score From Retinal Fundus Images With Deep Learning Algorithms
Open Access
- 28 January 2020
- journal article
- research article
- Published by Association for Research in Vision and Ophthalmology (ARVO) in Translational Vision Science & Technology
- Vol. 9 (2), 28
- https://doi.org/10.1167/tvst.9.2.28
Abstract
Purpose: To evaluate high accumulation of coronary artery calcium (CAC) from retinal fundus images with deep learning technologies as an inexpensive and radiation-free screening method. Methods: Individuals who underwent bilateral retinal fundus imaging and CAC score (CACS) evaluation from coronary computed tomography scans on the same day were identified. With this database, performances of deep learning algorithms (inception-v3) to distinguish high CACS from CACS of 0 were evaluated at various thresholds for high CACS. Vessel-inpainted and fovea-inpainted images were also used as input to investigate areas of interest in determining CACS. Results: A total of 44,184 images from 20,130 individuals were included. A deep learning algorithm for discrimination of no CAC from CACS >100 achieved area under receiver operating curve (AUROC) of 82.3% (79.5%–85.0%) and 83.2% (80.2%–86.3%) using unilateral and bilateral fundus images, respectively, under a 5-fold cross validation setting. AUROC increased as the criterion for high CACS was increased, showing a plateau at 100 and losing significant improvement thereafter. AUROC decreased when fovea was inpainted and decreased further when vessels were inpainted, whereas AUROC increased when bilateral images were used as input. Conclusions: Visual patterns of retinal fundus images in subjects with CACS > 100 could be recognized by deep learning algorithms compared with those with no CAC. Exploiting bilateral images improves discrimination performance, and ablation studies removing retinal vasculature or fovea suggest that recognizable patterns reside mainly in these areas. Translational Relevance: Retinal fundus images can be used by deep learning algorithms for prediction of high CACS.Keywords
This publication has 33 references indexed in Scilit:
- Development and Validation of Deep Learning Models for Screening Multiple Abnormal Findings in Retinal Fundus ImagesOphthalmology, 2020
- Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic RetinopathyOphthalmology, 2018
- Blood vessel segmentation algorithms - Review of methods, datasets and evaluation metricsComputer Methods and Programs in Biomedicine, 2018
- A survey on deep learning in medical image analysisMedical Image Analysis, 2017
- Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort studyBMJ, 2017
- Retinal Vessel Calibers in Predicting Long-Term Cardiovascular OutcomesCirculation, 2016
- Role of Nonenhanced Multidetector CT Coronary Artery Calcium Testing in Asymptomatic and Symptomatic IndividualsRadiology, 2012
- Risk Prediction of Coronary Heart Disease Based on Retinal Vascular Caliber (from the Atherosclerosis Risk In Communities [ARIC] Study)The American Journal of Cardiology, 2008
- Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE projectEuropean Heart Journal, 2003
- Improving Coronary Heart Disease Risk Assessment in Asymptomatic PeopleCirculation, 2001