Observational studyTranslational vision science & technology2020
Factors in Color Fundus Photographs That Can Be Used by Humans to Determine Sex of Individuals.
Observational study in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Association between fundus sex index and hypertension, diabetes mellitus, and smoking in the Kumejima study.Japanese journal of ophthalmology · 2026Observational
- Deep Ensemble Learning to Detect Retinal Vascular Leakage on Ultrawide-Field Fundus Photographs of Patients With Uveitis.Translational vision science & technology · 2026Article
- DREAM: A framework for discovering mechanisms underlying AI prediction of protected attributes.medRxiv : the preprint server for health sciences · 2025Article
- Associations between retinal thickness and background factors in eyes without retinal diseases.Scientific reports · 2025Article
- Refractive error and axial length prediction using fundus parameters of normal eyes in the Kumejima population study.Japanese journal of ophthalmology · 2025Observational
- Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians.Nature biomedical engineering · 2025Article
- Age prediction using fundus parameters of normal eyes from the Kumejima population study.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Article
- Relationship between fundus sex index obtained using color fundus parameters and body height or axial length in the Kumejima population.Japanese journal of ophthalmology · 2024Observational
- Sex differences in choroidal vessels using novel wide-field choroidal en-face images from optical coherence tomography.Scientific reports · 2024Article
- Convolutional Neural Network-Based Prediction of Axial Length Using Color Fundus Photography.Translational vision science & technology · 2024Article
- AI-Human Hybrid Workflow Enhances Teleophthalmology for the Detection of Diabetic Retinopathy.Ophthalmology science · 2023Article
- Artificial intelligence, explainability, and the scientific method: A proof-of-concept study on novel retinal biomarker discovery.PNAS nexus · 2023Article
- Sex determination using color fundus parameters in older adults of Kumejima population study.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023Article
- Three-year changes in sex judgment using color fundus parameters in elementary school students.PloS one · 2023Observational
- Artificial Intelligence in Predicting Systemic Parameters and Diseases From Ophthalmic Imaging.Frontiers in digital health · 2022Review
- Predicting sex from retinal fundus photographs using automated deep learning.Scientific reports · 2021Article
- Recent Advances and Clinical Application of Color Scanning Laser Ophthalmoscope.Journal of clinical medicine · 2021Review
- Systematic Comparison of Heatmapping Techniques in Deep Learning in the Context of Diabetic Retinopathy Lesion Detection.Translational vision science & technology · 2020Article
- Sex judgment using color fundus parameters in elementary school students.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2020Observational
- Author Response: Factors in Color Fundus Photographs That Can Be Used by Humans to Determine Sex of Individuals.Translational vision science & technology · 2020Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Artificial intelligence (AI) can identify the sex of an individual from color fundus photographs (CFPs). However, the mechanism(s) involved in this identification has not been determined. This study was conducted to determine the information in CFPs that can be used to determine the sex of an individual. Methods: Prospective observational cross-sectional study of 112 eyes of 112 healthy volunteers. The following characteristics of CFPs were analyzed: the color of peripapillary area expressed by the mean values of red, green, and blue intensities, and the tessellation expressed by the tessellation fundus index (TFI). The optic disc ovality ratio, papillomacular angle, retinal artery trajectory, and retinal vessel angles were also quantified. Their differences between the sexes were assessed by Mann-Whitney Results: The mean age of 76 men and 36 women was 25.8 years. The regularized binomial logistic regression delivered the optimal model for sex selected variables of peripapillary temporal green and blue intensities, temporal TFI, supratemporal TFI, optic disc ovality ratio, artery trajectory, and supratemporal retinal artery angle. With this approach, the discrimination accuracy rate was 77.9%. Conclusions: Human-assessed characteristics of CFPs are useful in investigating the new theme proposed by AI, the sex of an individual. Translational Relevance: This is the first report to approach the thinking process of AI by humans and can be a new approach to medical AI research.
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