Gender Prediction for a Multiethnic Population via Deep Learning Across Different Retinal Fundus Photograph Fields: Retrospective Cross-sectional Study.
Bjorn Kaijun BetzlerHenrik Hee Seung YangSahil ThakurMarco YuTen Cheer QuekZhi Da SohGeunyoung LeeYih-Chung ThamTien-Yin WongTyler Hyung Taek RimChing-Yu ChengPublished in: JMIR medical informatics (2021)
We confirmed that gender among the Asian population can be predicted with fundus photographs by using deep learning, and our algorithms' performance in terms of gender prediction differed according to the field of fundus photographs, age subgroups, and ethnic groups. Our work provides a further understanding of using deep learning models for the prediction of gender-related diseases. Further validation of our findings is still needed.