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Background Deep learning algorithms have been built for the detection of systemic and eye diseases based on fundus photographs. The retina possesses features that can be affected by gender differences, and the extent to which these features are captured via photography differs depending on the retinal image field. Objective We aimed to compare deep learning algorithms’ performance in predicting gender based on different fields of fundus photographs (optic disc–centered, macula-centered, and peripheral fields). Methods This retrospective cross-sectional study included 172,170 fundus photographs of 9956 adults aged ≥40 years from the Singapore Epidemiology of Eye Diseases Study. Optic disc–centered, macula-centered, and peripheral field fundus images were included in this study as input data for a deep learning model for gender prediction. Performance was estimated at the individual level and image level. Receiver operating characteristic curves for binary classification were calculated. Results The deep learning algorithms predicted gender with an area under the receiver operating characteristic curve (AUC) of 0.94 at the individual level and an AUC of 0.87 at the image level. Across the three image field types, the best performance was seen when using optic disc–centered field images (younger subgroups: AUC=0.91; older subgroups: AUC=0.86), and algorithms that used peripheral field images had the lowest performance (younger subgroups: AUC=0.85; older subgroups: AUC=0.76). Across the three ethnic subgroups, algorithm performance was lowest in the Indian subgroup (AUC=0.88) compared to that in the Malay (AUC=0.91) and Chinese (AUC=0.91) subgroups when the algorithms were tested on optic disc–centered images. Algorithms’ performance in gender prediction at the image level was better in younger subgroups (aged <65 years; AUC=0.89) than in older subgroups (aged ≥65 years; AUC=0.82). Conclusions 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.

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BACKGROUND Deep Learning (DL) algorithms have been built for detection of systemic and eye diseases from retinal photographs. The retina possesses features which can be affected by gender differences, and the extent to which these features are captured upon photography differs depending on the retinal image field. OBJECTIVE To compare DL algorithms’ performance in predicting gender when using different fields of retinal photographs (disc-centered, macula-centered, peripheral). METHODS This retrospective cross-sectional study included 172,170 retinal photographs from 9956 adults aged ≥ 40 years from the Singapore Epidemiology of Eye Diseases (SEED) Study. Optic disc-centered, macula-centered and peripheral field retinal fundus images were included in this study as input to a DL model for gender prediction. Performance was estimated at individual level and image level. Receiver operating characteristic (ROC) curves for binary classification were calculated. RESULTS The DL algorithms predicted gender with area under the ROC (AUC) of 0.94 at individual-level and AUC of 0.87 at image-level. Across the three image fields, the best performance was seen in disc-centered (AUC: 0.91 in younger and 0.86 in older age subgroups), and peripheral field images showed the lowest performance (AUC: 0.85 in younger and 0.76 in older subgroups). Between the three ethnic subgroups, performance was lowest in the Indian subgroup (AUC: 0.88) compared to Malay (AUC: 0.91) and Chinese (AUC: 0.91) when tested on disc-centered images. The performance of gender prediction at the image level was better in younger age subgroups of < 65 years (AUC: 0.89) than in older age subgroups of ≥ 65 years (AUC: 0.82). CONCLUSIONS We confirmed that gender can be predicted from retinal photographs using DL in Asian population, and the performance of gender prediction differ according to field of retinal photographs, age-subgroups, and ethnic groups. Our work provides a further understanding of using DL models for prediction of gender-related diseases. Further validation of our findings is still needed.