ArticleOphthalmology2022
DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity.
Article in Ophthalmology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- YOLOv5 Attention Analysis for Anterior Eye Disease Classification: Grad-CAM++ Feature Importance and Cut-and-Paste Validation.Journal of imaging informatics in medicine · 2026Article
- Precision Livestock Farming and Biomedical Engineering: Assessing Feed Quality, Animal Health, and Behavior Using Machine Learning for Sensor Data.Sensors (Basel, Switzerland) · 2026Review
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- Report Generation System for Slit-Lamp Image Interpretation Using Vision-Language Models.Ophthalmology and therapy · 2026Article
- Prediction of Cataract Severity Using Slit Lamp Images from a Portable Smartphone Device: A Pilot Study.Sensors (Basel, Switzerland) · 2026Article
- Automated Report Generation in Ophthalmology: Integrating Artificial Intelligence, Multimodal Imaging, and Clinical Data.Ophthalmology and therapy · 2026Review
- Global burden and cross-country inequalities of age-related eye diseases from 1990 to 2021: a comprehensive analysis of temporal trends and socioeconomic disparities.Eye and vision (London, England) · 2026Article
- Spontaneous refractive error, ocular biometry and age related lens changes in a population of geriatric rhesus macaques.Scientific reports · 2025Article
- MRI multi-sequence deep learning integration with clinical profiles for pediatric viral encephalitis diagnosis.Scientific reports · 2025Article
- GAN-Enhanced Hybrid Deep Learning with Explainable AI for Automated Cataract Diagnosis.Journal of medical systems · 2025Article
- A Deep Learning Model for Detecting the Eyes Receiving Glaucoma Medications Using Anterior Segment Images.Translational vision science & technology · 2025Article
- Towards Accountable AI in Eye Disease Diagnosis: Workflow, External Validation, and Development.ArXiv · 2025Article
- AI Workflow, External Validation, and Development in Eye Disease Diagnosis.JAMA network open · 2025Article
- Far-Red, High-Resolution, Reflection-Free Images of the Anterior Segment in Retro-Illumination.Translational vision science & technology · 2025Article
- LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models.Findings of ACL. NAACL · 2025Article
- Artificial Intelligence Applications in Ophthalmology.JMA journal · 2025Review
- Multimodal AI diagnostic system for neuromyelitis optica based on ultrawide-field fundus photography.Frontiers in medicine · 2025Article
- Tackling visual impairment: emerging avenues in ophthalmology.Frontiers in medicine · 2025Review
- Automatic diagnosis of extraocular muscle palsy based on machine learning and diplopia images.International journal of ophthalmology · 2025Article
- Deep learning-driven approach for cataract management: towards precise identification and predictive analytics.Frontiers in cell and developmental biology · 2025Review
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Authors and funding
29 authors.
Funding
Abstract
purposeTo develop deep learning models to perform automated diagnosis and quantitative classification of age-related cataract from anterior segment photographs.
designDeepLensNet was trained by applying deep learning models to the Age-Related Eye Disease Study (AREDS) dataset.
participantsA total of 18 999 photographs (6333 triplets) from longitudinal follow-up of 1137 eyes (576 AREDS participants).
methodsDeep learning models were trained to detect and quantify nuclear sclerosis (NS; scale 0.9-7.1) from 45-degree slit-lamp photographs and cortical lens opacity (CLO; scale 0%-100%) and posterior subcapsular cataract (PSC; scale 0%-100%) from retroillumination photographs. DeepLensNet performance was compared with that of 14 ophthalmologists and 24 medical students.
main outcome measuresMean squared error (MSE).
resultsOn the full test set, mean MSE for DeepLensNet was 0.23 (standard deviation [SD], 0.01) for NS, 13.1 (SD, 1.6) for CLO, and 16.6 (SD, 2.4) for PSC. On a subset of the test set (substantially enriched for positive cases of CLO and PSC), for NS, mean MSE for DeepLensNet was 0.23 (SD, 0.02), compared with 0.98 (SD, 0.24; P = 0.000001) for the ophthalmologists and 1.24 (SD, 0.34; P = 0.000005) for the medical students. For CLO, mean MSE was 53.5 (SD, 14.8), compared with 134.9 (SD, 89.9; P = 0.003) for the ophthalmologists and 433.6 (SD, 962.1; P = 0.0007) for the medical students. For PSC, mean MSE was 171.9 (SD, 38.9), compared with 176.8 (SD, 98.0; P = 0.67) for the ophthalmologists and 398.2 (SD, 645.4; P = 0.18) for the medical students. In external validation on the Singapore Malay Eye Study (sampled to reflect the cataract severity distribution in AREDS), the MSE for DeepSeeNet was 1.27 for NS and 25.5 for PSC.
conclusionsDeepLensNet performed automated and quantitative classification of cataract severity for all 3 types of age-related cataract. For the 2 most common types (NS and CLO), the accuracy was significantly superior to that of ophthalmologists; for the least common type (PSC), it was similar. DeepLensNet may have wide potential applications in both clinical and research domains. In the future, such approaches may increase the accessibility of cataract assessment globally. The code and models are available at https://github.com/ncbi/deeplensnet.
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