Cargando…
Predicting CT-Based Coronary Artery Disease Using Vascular Biomarkers Derived from Fundus Photographs with a Graph Convolutional Neural Network
The study population contains 145 patients who were prospectively recruited for coronary CT angiography (CCTA) and fundoscopy. This study first examined the association between retinal vascular changes and the Coronary Artery Disease Reporting and Data System (CAD-RADS) as assessed on CCTA. Then, we...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9221688/ https://www.ncbi.nlm.nih.gov/pubmed/35741200 http://dx.doi.org/10.3390/diagnostics12061390 |
_version_ | 1784732683908153344 |
---|---|
author | Huang, Fan Lian, Jie Ng, Kei-Shing Shih, Kendrick Vardhanabhuti, Varut |
author_facet | Huang, Fan Lian, Jie Ng, Kei-Shing Shih, Kendrick Vardhanabhuti, Varut |
author_sort | Huang, Fan |
collection | PubMed |
description | The study population contains 145 patients who were prospectively recruited for coronary CT angiography (CCTA) and fundoscopy. This study first examined the association between retinal vascular changes and the Coronary Artery Disease Reporting and Data System (CAD-RADS) as assessed on CCTA. Then, we developed a graph neural network (GNN) model for predicting the CAD-RADS as a proxy for coronary artery disease. The CCTA scans were stratified by CAD-RADS scores by expert readers, and the vascular biomarkers were extracted from their fundus images. Association analyses of CAD-RADS scores were performed with patient characteristics, retinal diseases, and quantitative vascular biomarkers. Finally, a GNN model was constructed for the task of predicting the CAD-RADS score compared to traditional machine learning (ML) models. The experimental results showed that a few retinal vascular biomarkers were significantly associated with adverse CAD-RADS scores, which were mainly pertaining to arterial width, arterial angle, venous angle, and fractal dimensions. Additionally, the GNN model achieved a sensitivity, specificity, accuracy and area under the curve of 0.711, 0.697, 0.704 and 0.739, respectively. This performance outperformed the same evaluation metrics obtained from the traditional ML models (p < 0.05). The data suggested that retinal vasculature could be a potential biomarker for atherosclerosis in the coronary artery and that the GNN model could be utilized for accurate prediction. |
format | Online Article Text |
id | pubmed-9221688 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92216882022-06-24 Predicting CT-Based Coronary Artery Disease Using Vascular Biomarkers Derived from Fundus Photographs with a Graph Convolutional Neural Network Huang, Fan Lian, Jie Ng, Kei-Shing Shih, Kendrick Vardhanabhuti, Varut Diagnostics (Basel) Article The study population contains 145 patients who were prospectively recruited for coronary CT angiography (CCTA) and fundoscopy. This study first examined the association between retinal vascular changes and the Coronary Artery Disease Reporting and Data System (CAD-RADS) as assessed on CCTA. Then, we developed a graph neural network (GNN) model for predicting the CAD-RADS as a proxy for coronary artery disease. The CCTA scans were stratified by CAD-RADS scores by expert readers, and the vascular biomarkers were extracted from their fundus images. Association analyses of CAD-RADS scores were performed with patient characteristics, retinal diseases, and quantitative vascular biomarkers. Finally, a GNN model was constructed for the task of predicting the CAD-RADS score compared to traditional machine learning (ML) models. The experimental results showed that a few retinal vascular biomarkers were significantly associated with adverse CAD-RADS scores, which were mainly pertaining to arterial width, arterial angle, venous angle, and fractal dimensions. Additionally, the GNN model achieved a sensitivity, specificity, accuracy and area under the curve of 0.711, 0.697, 0.704 and 0.739, respectively. This performance outperformed the same evaluation metrics obtained from the traditional ML models (p < 0.05). The data suggested that retinal vasculature could be a potential biomarker for atherosclerosis in the coronary artery and that the GNN model could be utilized for accurate prediction. MDPI 2022-06-04 /pmc/articles/PMC9221688/ /pubmed/35741200 http://dx.doi.org/10.3390/diagnostics12061390 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Huang, Fan Lian, Jie Ng, Kei-Shing Shih, Kendrick Vardhanabhuti, Varut Predicting CT-Based Coronary Artery Disease Using Vascular Biomarkers Derived from Fundus Photographs with a Graph Convolutional Neural Network |
title | Predicting CT-Based Coronary Artery Disease Using Vascular Biomarkers Derived from Fundus Photographs with a Graph Convolutional Neural Network |
title_full | Predicting CT-Based Coronary Artery Disease Using Vascular Biomarkers Derived from Fundus Photographs with a Graph Convolutional Neural Network |
title_fullStr | Predicting CT-Based Coronary Artery Disease Using Vascular Biomarkers Derived from Fundus Photographs with a Graph Convolutional Neural Network |
title_full_unstemmed | Predicting CT-Based Coronary Artery Disease Using Vascular Biomarkers Derived from Fundus Photographs with a Graph Convolutional Neural Network |
title_short | Predicting CT-Based Coronary Artery Disease Using Vascular Biomarkers Derived from Fundus Photographs with a Graph Convolutional Neural Network |
title_sort | predicting ct-based coronary artery disease using vascular biomarkers derived from fundus photographs with a graph convolutional neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9221688/ https://www.ncbi.nlm.nih.gov/pubmed/35741200 http://dx.doi.org/10.3390/diagnostics12061390 |
work_keys_str_mv | AT huangfan predictingctbasedcoronaryarterydiseaseusingvascularbiomarkersderivedfromfundusphotographswithagraphconvolutionalneuralnetwork AT lianjie predictingctbasedcoronaryarterydiseaseusingvascularbiomarkersderivedfromfundusphotographswithagraphconvolutionalneuralnetwork AT ngkeishing predictingctbasedcoronaryarterydiseaseusingvascularbiomarkersderivedfromfundusphotographswithagraphconvolutionalneuralnetwork AT shihkendrick predictingctbasedcoronaryarterydiseaseusingvascularbiomarkersderivedfromfundusphotographswithagraphconvolutionalneuralnetwork AT vardhanabhutivarut predictingctbasedcoronaryarterydiseaseusingvascularbiomarkersderivedfromfundusphotographswithagraphconvolutionalneuralnetwork |