ArticleScientific reports2023
An interpretable and interactive deep learning algorithm for a clinically applicable retinal fundus diagnosis system by modelling finding-disease relationship.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
- Evaluating an AI-assisted triage workflow for retinal diseases.PLOS digital health · 2026Article
- Explainable deep learning framework incorporating medical knowledge for insulin titration in diabetes.Communications medicine · 2026Article
- Artificial intelligence framework for multi-pathology risk assessment from retinal fundus images: deep learning approach to 15-disease screening.Frontiers in medicine · 2026Article
- A reliable and explainable deep learning framework for clinical-grade endocrine disorder risk prediction and decision support.Frontiers in artificial intelligence · 2026Article
- Detection and diagnosis of diabetic retinopathy in retinal fundus images using agentic AI approaches.Scientific reports · 2025Article
- Predicting branch retinal vein occlusion development using multimodal deep learning and pre-onset fundus hemisection images.Scientific reports · 2025Article
- Validation of neuron activation patterns for artificial intelligence models in oculomics.Scientific reports · 2024Article
- Development of a generative deep learning model to improve epiretinal membrane detection in fundus photography.BMC medical informatics and decision making · 2024Article
- OCT-based deep-learning models for the identification of retinal key signs.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of abnormal findings manifested in retinal fundus images and diagnosis of ophthalmic diseases are essential to the management of potentially vision-threatening eye conditions. Recently, deep learning-based computer-aided diagnosis systems (CADs) have demonstrated their potential to reduce reading time and discrepancy amongst readers. However, the obscure reasoning of deep neural networks (DNNs) has been the leading cause to reluctance in its clinical use as CAD systems. Here, we present a novel architectural and algorithmic design of DNNs to comprehensively identify 15 abnormal retinal findings and diagnose 8 major ophthalmic diseases from macula-centered fundus images with the accuracy comparable to experts. We then define a notion of counterfactual attribution ratio (CAR) which luminates the system's diagnostic reasoning, representing how each abnormal finding contributed to its diagnostic prediction. By using CAR, we show that both quantitative and qualitative interpretation and interactive adjustment of the CAD result can be achieved. A comparison of the model's CAR with experts' finding-disease diagnosis correlation confirms that the proposed model identifies the relationship between findings and diseases similarly as ophthalmologists do.
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