ArticleScientific reports2021
Predicting sex from retinal fundus photographs using automated deep learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
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Who cites it
75 citing papers in PubMed.
- Reduced AI-based sex classification performance in clinically unaffected fellow eyes of patients with polypoidal choroidal vasculopathy: comparison with central serous chorioretinopathy and controls.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmology.ACM transactions on computing for healthcare · 2026Article
- Quantifying regional contributions to sex classification from fundus photographs via a two-stage attention-based deep learning approach.Scientific reports · 2026Article
- Distribution of axial length in Japanese children and adolescents aged 4 to 19 years.Japanese journal of ophthalmology · 2026Observational
- Artificial intelligence in genomic medicine: dispelling three myths.NPJ genomic medicine · 2026Article
- Deep learning detection of retinitis pigmentosa inheritance forms through synthetic data expansion of a rare disease dataset.Scientific reports · 2026Article
- Deep Ensemble Learning to Detect Retinal Vascular Leakage on Ultrawide-Field Fundus Photographs of Patients With Uveitis.Translational vision science & technology · 2026Article
- Evolution of Oculomics: From Naked Eye Observations to Artificial Intelligence over 100 Years.Ophthalmology science · 2026Article
- Retinal photographs to predict life's essential 8 for cardiovascular risk stratification: a novel deep-learning-based tool.European heart journal. Digital health · 2026Article
- Artificial Intelligence in Ocular Oncology: Differentiating Choroidal Melanocytic Lesions.Ophthalmology science · 2026Article
- Posterior segment imaging clues to systemic malignancy: a phenotype-to-confirmation narrative review.Frontiers in ophthalmology · 2026Review
- Deep Learning Detection of Retinitis Pigmentosa Inheritance Forms through Synthetic Data Expansion of a Rare Disease Dataset.Research square · 2025Article
- Clinical Applications of Artificial Intelligence in Uveal Melanoma.Anticancer research · 2025Review
- Article
- Protocol for Retinal Imaging and Extraction of Imaging-Derived Phenotypes from the Chinese Human Phenome Project.Phenomics (Cham, Switzerland) · 2025Article
- Deep learning-based prediction of cardiopulmonary disease in retinal images of premature infants.medRxiv : the preprint server for health sciences · 2025Article
- The march to harmonized imaging standards for retinal imaging.Progress in retinal and eye research · 2025Review
- Deep Learning Study of Alkaptonuria Spinal Disease Assesses Global and Regional Severity and Detects Occult Treatment Status.Journal of inherited metabolic disease · 2025Article
- Applying artificial intelligence to rare diseases: a literature review highlighting lessons from Fabry disease.Orphanet journal of rare diseases · 2025Review
- Recent Advancements in Robotic-assisted Plastic Surgery Procedures: A Systematic Review.Plastic and reconstructive surgery. Global open · 2025Article
15 more citing papers are in PubMed but not listed here.
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10 authors.
Funding
Abstract
Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. Herein we present the development of a deep learning model by clinicians without coding, which predicts reported sex from retinal fundus photographs. A model was trained on 84,743 retinal fundus photos from the UK Biobank dataset. External validation was performed on 252 fundus photos from a tertiary ophthalmic referral center. For internal validation, the area under the receiver operating characteristic curve (AUROC) of the code free deep learning (CFDL) model was 0.93. Sensitivity, specificity, positive predictive value (PPV) and accuracy (ACC) were 88.8%, 83.6%, 87.3% and 86.5%, and for external validation were 83.9%, 72.2%, 78.2% and 78.6% respectively. Clinicians are currently unaware of distinct retinal feature variations between males and females, highlighting the importance of model explainability for this task. The model performed significantly worse when foveal pathology was present in the external validation dataset, ACC: 69.4%, compared to 85.4% in healthy eyes, suggesting the fovea is a salient region for model performance OR (95% CI): 0.36 (0.19, 0.70) p = 0.0022. Automated machine learning (AutoML) may enable clinician-driven automated discovery of novel insights and disease biomarkers.
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