ArticleTranslational vision science & technology2021
Five-Category Intelligent Auxiliary Diagnosis Model of Common Fundus Diseases Based on Fundus Images.
Article in Translational vision science & technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Optic disc morphometrics as a potential ocular biomarker for depression: evidence from two cross-sectional cohort studies.Translational psychiatry · 2025Article
- Prevalence and Clinical Characteristics of Dark Without Pressure Retinal Changes in a Chinese Hospital-Based Population.Ophthalmology and therapy · 2025Article
- High burden of blindness at initial hospitalisation with primary angle-closure glaucoma in a national multicentre study in China.BMJ open ophthalmology · 2025Article
- Exploring potential imaging biomarkers for ischemic retinal vein occlusion: insights from fundus autofluorescence and optical coherence tomography.Frontiers in medicine · 2025Article
- A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images.Scientific reports · 2024Article
- Dry age-related macular degeneration classification from optical coherence tomography images based on ensemble deep learning architecture.Frontiers in medicine · 2024Article
- Quantitative analysis of retinal vascular parameters changes in school-age children with refractive error using artificial intelligence.Frontiers in medicine · 2024Article
- Intelligent diagnostic model for pterygium by combining attention mechanism and MobileNetV2.International journal of ophthalmology · 2024Article
- Application and visualization study of an intelligence-assisted classification model for common eye diseases using B-mode ultrasound images.Frontiers in neuroscience · 2024Article
- Quantitative analysis of optic disc changes in school-age children with ametropia based on artificial intelligence.International journal of ophthalmology · 2023Article
- Automated detection of myopic maculopathy using five-category models based on vision outlooker for visual recognition.Frontiers in computational neuroscience · 2023Article
- Research on classification method of high myopic maculopathy based on retinal fundus images and optimized ALFA-Mix active learning algorithm.International journal of ophthalmology · 2023Article
- Development and Application of an Intelligent Diagnosis System for Retinal Vein Occlusion Based on Deep Learning.Disease markers · 2022Article
- Classification of dry and wet macular degeneration based on the ConvNeXT model.Frontiers in computational neuroscience · 2022Article
- Pterygium Screening and Lesion Area Segmentation Based on Deep Learning.Journal of healthcare engineering · 2022Article
- Screening of Common Retinal Diseases Using Six-Category Models Based on EfficientNet.Frontiers in medicine · 2022Article
- Automated measurement of the disc-fovea angle based on DeepLabv3.Frontiers in neurology · 2022Article
- Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on the Application of Artificial Intelligence in Ophthalmic Disease Diagnosis.Frontiers in pharmacology · 2022Article
- Research on an Intelligent Lightweight-Assisted Pterygium Diagnosis Model Based on Anterior Segment Images.Disease markers · 2021Article
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Purpose: The discrepancy of the number between ophthalmologists and patients in China is large. Retinal vein occlusion (RVO), high myopia, glaucoma, and diabetic retinopathy (DR) are common fundus diseases. Therefore, in this study, a five-category intelligent auxiliary diagnosis model for common fundus diseases is proposed; the model's area of focus is marked. Methods: A total of 2000 fundus images were collected; 3 different 5-category intelligent auxiliary diagnosis models for common fundus diseases were trained via different transfer learning and image preprocessing techniques. A total of 1134 fundus images were used for testing. The clinical diagnostic results were compared with the diagnostic results. The main evaluation indicators included sensitivity, specificity, F1-score, area under the concentration-time curve (AUC), 95% confidence interval (CI), kappa, and accuracy. The interpretation methods were used to obtain the model's area of focus in the fundus image. Results: The accuracy rates of the 3 intelligent auxiliary diagnosis models on the 1134 fundus images were all above 90%, the kappa values were all above 88%, the diagnosis consistency was good, and the AUC approached 0.90. For the 4 common fundus diseases, the best results of sensitivity, specificity, and F1-scores of the 3 models were 88.27%, 97.12%, and 84.02%; 89.94%, 99.52%, and 93.90%; 95.24%, 96.43%, and 85.11%; and 88.24%, 98.21%, and 89.55%, respectively. Conclusions: This study designed a five-category intelligent auxiliary diagnosis model for common fundus diseases. It can be used to obtain the diagnostic category of fundus images and the model's area of focus. Translational Relevance: This study will help the primary doctors to provide effective services to all ophthalmologic patients.
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