ArticleDiagnostics (Basel, Switzerland)2022
Predicting CT-Based Coronary Artery Disease Using Vascular Biomarkers Derived from Fundus Photographs with a Graph Convolutional Neural Network.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Predictive Modeling of Coronary Artery Disease Using Color Fundus Photography-Based Features of Retinal Vasculature.Ophthalmology and therapy · 2026Article
- Prediction of coronary artery disease using retinal optical coherence tomography angiography imaging and electronic health records: a multimodal machine learning approach.BMC cardiovascular disorders · 2026Article
- Through the eye to the heart: a scoping review of artificial intelligence in retinal imaging for cardiovascular disease assessment.BMC medical informatics and decision making · 2025Article
- High-resolution fundus images for ophthalmomics and early cardiovascular disease prediction.Scientific data · 2025Article
- Integrating electrocardiogram and fundus images for early detection of cardiovascular diseases.Scientific reports · 2025Article
- Research advances on artificial intelligence assisted diagnosis and risk assessment in cardiovascular disease using retinal imaging.Frontiers in cardiovascular medicine · 2025Review
- Assessing the Correlation Between Retinal Arteriolar Bifurcation Parameters and Coronary Atherosclerosis.Ophthalmology and therapy · 2024Article
- Retinal Imaging-Based Oculomics: Artificial Intelligence as a Tool in the Diagnosis of Cardiovascular and Metabolic Diseases.Biomedicines · 2024Review
- DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features.Sensors (Basel, Switzerland) · 2024Article
- Fusion Modeling: Combining Clinical and Imaging Data to Advance Cardiac Care.Circulation. Cardiovascular imaging · 2023Review
- Deep convolutional neural networks for multiple histologic types of ovarian tumors classification in ultrasound images.Frontiers in oncology · 2023Article
- An Overview of Deep-Learning-Based Methods for Cardiovascular Risk Assessment with Retinal Images.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.