ArticleEClinicalMedicine2022
A cascade eye diseases screening system with interpretability and expandability in ultra-wide field fundus images: A multicentre diagnostic accuracy study.
Article in EClinicalMedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic accuracy of deep learning using ultra-widefield fundus imaging for retinal detachment: a systematic review and meta-analysis.BMC ophthalmology · 2026Pooled it
- AI for glaucoma, Are we reporting well? a systematic literature review of DECIDE-AI checklist adherence.Eye (London, England) · 2025Pooled it
- Automatic diagnosis of retinoblastoma and quantitative analysis of retinal vascular morphology using deep learning.Frontiers in cell and developmental biology · 2026Article
- Ultra-wide-field fundus photography and AI-based screening and referral for multiple ocular fundus diseases.Cell reports. Medicine · 2025Article
- Radiomics-Based OCT Analysis of Choroid Reveals Biomarkers of Central Serous Chorioretinopathy.Translational vision science & technology · 2025Article
- Development and evaluation of a deep learning system for screening real-world multiple abnormal findings based on ultra-widefield fundus images.Frontiers in medicine · 2025Article
- Multimodal Imaging-Based Cerebral Blood Flow Prediction Model Development in Simulated Microgravity.Cyborg and bionic systems (Washington, D.C.) · 2025Article
- Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review.Ophthalmology and therapy · 2024Review
- An artificial intelligence system for the whole process from diagnosis to treatment suggestion of ischemic retinal diseases.Cell reports. Medicine · 2023Article
- Radiomics-Based Prediction of Anti-VEGF Treatment Response in Neovascular Age-Related Macular Degeneration With Pigment Epithelial Detachment.Translational vision science & technology · 2023Article
- Personalized Medicine in Ophthalmic Diseases: Challenges and Opportunities.Journal of personalized medicine · 2023Article
- Deep Learning for the Detection of Multiple Fundus Diseases Using Ultra-widefield Images.Ophthalmology and therapy · 2023Article
- Comparing the Clinical Viability of Automated Fundus Image Segmentation Methods.Sensors (Basel, Switzerland) · 2022Article
- A Generalized and Interpretable Multi-Label Multi-Disease Screening System for Ocular Anterior Segment Disease Detection.Ophthalmology scienceArticle
- Diabetic Retinopathy Assessment through Multitask Learning Approach on Heterogeneous Fundus Image Datasets.Ophthalmology scienceArticle
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Authors and funding
11 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Clinical application of artificial intelligence is limited due to the lack of interpretability and expandability in complex clinical settings. We aimed to develop an eye diseases screening system with improved interpretability and expandability based on a lesion-level dissection and tested the clinical expandability and auxiliary ability of the system. Methods: The four-hierarchical interpretable eye diseases screening system (IEDSS) based on a novel structural pattern named lesion atlas was developed to identify 30 eye diseases and conditions using a total of 32,026 ultra-wide field images collected from the Second Affiliated Hospital of Zhejiang University, School of Medicine (SAHZU), the First Affiliated Hospital of University of Science and Technology of China (FAHUSTC), and the Affiliated People's Hospital of Ningbo University (APHNU) in China between November 1, 2016 to February 28, 2022. The performance of IEDSS was compared with ophthalmologists and classic models trained with image-level labels. We further evaluated IEDSS in two external datasets, and tested it in a real-world scenario and an extended dataset with new phenotypes beyond the training categories. The accuracy (ACC), F1 score and confusion matrix were calculated to assess the performance of IEDSS. Findings: IEDSS reached average ACCs (aACC) of 0·9781 (95%CI 0·9739-0·9824), 0·9660 (95%CI 0·9591-0·9730) and 0·9709 (95%CI 0·9655-0·9763), frequency-weighted average F1 scores of 0·9042 (95%CI 0·8957-0·9127), 0·8837 (95%CI 0·8714-0·8960) and 0·8874 (95%CI 0·8772-0·8972) in datasets of SAHZU, APHNU and FAHUSTC, respectively. IEDSS reached a higher aACC (0·9781, 95%CI 0·9739-0·9824) compared with a multi-class image-level model (0·9398, 95%CI 0·9329-0·9467), a classic multi-label image-level model (0·9278, 95%CI 0·9189-0·9366), a novel multi-label image-level model (0·9241, 95%CI 0·9151-0·9331) and a lesion-level model without Adaboost (0·9381, 95%CI 0·9299-0·9463). In the real-world scenario, the aACC of IEDSS (0·9872, 95%CI 0·9828-0·9915) was higher than that of the senior ophthalmologist (SO) (0·9413, 95%CI 0·9321-0·9504, p = 0·000) and the junior ophthalmologist (JO) (0·8846, 95%CI 0·8722-0·8971, p = 0·000). IEDSS remained strong performance (ACC = 0·8560, 95%CI 0·8252-0·8868) compared with JO (ACC = 0·784, 95%CI 0·7479-0·8201, p= 0·003) and SO (ACC = 0·8500, 95%CI 0·8187-0·8813, Interpretation: IEDSS showed excellent and stable performance in identifying common eye conditions and conditions beyond the training categories. The transparency and expandability of IEDSS could tremendously increase the clinical application range and the practical clinical value of it. It would enhance the efficiency and reliability of clinical practice, especially in remote areas with a lack of experienced specialists. Funding: National Natural Science Foundation Regional Innovation and Development Joint Fund (U20A20386), Key research and development program of Zhejiang Province (2019C03020), Clinical Medical Research Centre for Eye Diseases of Zhejiang Province (2021E50007).
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