| Title: | Factors in Color Fundus Photographs That Can Be Used by Humans to Determine Sex of Individuals |
| Journal: | Transl Vis Sci Technol |
| Published: | 1 Jan 2020 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/32518709/ |
| DOI: | https://doi.org/10.1167/tvst.9.2.4 |
Publication 3610
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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 U tests. Regularized binomial logistic regression was used to select the decisive factors. In addition, its discriminative performance was evaluated through the leave-one-out cross validation.
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.
1 Author
- Yamashita T et al.
1 Application
| Application ID | Title |
|---|---|
| 43140 | Evaluation of deep learning in capturing epistatic effect |
Enabling scientific discoveries that improve human health