Predicting sex from retinal fundus photographs using automated deep learning

Korot, Edward; Pontikos, Nikolas; Liu, Xiaoxuan; Wagner, Siegfried; Faes, Livia; Huemer, Josef; Balaskas, Konstantinos; Denniston, Alastair K.O.; Khawaja, Anthony P; Keane, Pearse A.

doi:10.1038/s41598-021-89743-x

Cited by 96 publications

(74 citation statements)

References 42 publications

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“…Optic disc omission was not described, although their reported heat maps indicated activations in the fovea, optic disc, and retinal vessels [ 22 ]. In addition, Korot et al [ 41 ] reported poor performance when using images with foveal pathologies and used this finding to suggest that the fovea is an important input region for gender prediction. However, their saliency maps strongly attributed their model’s predictive power to the optic disc.…”

Section: Discussionmentioning

confidence: 99%

Gender Prediction for a Multiethnic Population via Deep Learning Across Different Retinal Fundus Photograph Fields: Retrospective Cross-sectional Study

Betzler¹,

Yang²,

Thakur³

et al. 2021

JMIR Med Inform

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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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Section: Discussionmentioning

confidence: 99%

Gender Prediction for a Multiethnic Population via Deep Learning Across Different Retinal Fundus Photograph Fields: Retrospective Cross-sectional Study

Betzler¹,

Yang²,

Thakur³

et al. 2021

JMIR Med Inform

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“…a change in the way an image looks over time. The degree to which a retinal image can be used to identify a person, especially when that retina looks different over time with age, 64 the presence of disease, 65 and with different cameras, is unclear.…”

Section: Privacymentioning

confidence: 99%

Deepfakes in Ophthalmology

Chen

Coyner

Chan

et al. 2021

Ophthalmology Science

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“…In contrast, automated machine learning (AutoML) techniques seek to accomplish these steps without user input. Recent studies assessing the feasibility of AutoML in healthcare have found promising results in comparison to bespoke models [11][12][13][14]. This represents an opportunity to enable clinicians with no computational background to leverage the power of ML.…”

Section: Introductionmentioning

confidence: 99%

Evaluating an automated machine learning model that predicts visual acuity outcomes in patients with neovascular age-related macular degeneration

Abbas

O’Byrne

et al. 2022

Graefes Arch Clin Exp Ophthalmol

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Purpose Neovascular age-related macular degeneration (nAMD) is a major global cause of blindness. Whilst anti-vascular endothelial growth factor (anti-VEGF) treatment is effective, response varies considerably between individuals. Thus, patients face substantial uncertainty regarding their future ability to perform daily tasks. In this study, we evaluate the performance of an automated machine learning (AutoML) model which predicts visual acuity (VA) outcomes in patients receiving treatment for nAMD, in comparison to a manually coded model built using the same dataset. Furthermore, we evaluate model performance across ethnic groups and analyse how the models reach their predictions. Methods Binary classification models were trained to predict whether patients’ VA would be ‘Above’ or ‘Below’ a score of 70 one year after initiating treatment, measured using the Early Treatment Diabetic Retinopathy Study (ETDRS) chart. The AutoML model was built using the Google Cloud Platform, whilst the bespoke model was trained using an XGBoost framework. Models were compared and analysed using the What-if Tool (WIT), a novel model-agnostic interpretability tool. Results Our study included 1631 eyes from patients attending Moorfields Eye Hospital. The AutoML model (area under the curve [AUC], 0.849) achieved a highly similar performance to the XGBoost model (AUC, 0.847). Using the WIT, we found that the models over-predicted negative outcomes in Asian patients and performed worse in those with an ethnic category of Other. Baseline VA, age and ethnicity were the most important determinants of model predictions. Partial dependence plot analysis revealed a sigmoidal relationship between baseline VA and the probability of an outcome of ‘Above’. Conclusion We have described and validated an AutoML-WIT pipeline which enables clinicians with minimal coding skills to match the performance of a state-of-the-art algorithm and obtain explainable predictions.

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Predicting sex from retinal fundus photographs using automated deep learning

Cited by 96 publications

References 42 publications

Gender Prediction for a Multiethnic Population via Deep Learning Across Different Retinal Fundus Photograph Fields: Retrospective Cross-sectional Study

Gender Prediction for a Multiethnic Population via Deep Learning Across Different Retinal Fundus Photograph Fields: Retrospective Cross-sectional Study

Deepfakes in Ophthalmology

Evaluating an automated machine learning model that predicts visual acuity outcomes in patients with neovascular age-related macular degeneration

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