Orbit (Amsterdam, Netherlands) · 2025
Functional blepharoptosis screening with generative augmented deep learning from external ocular photography.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Tan Licia, Lim Gilbert, Leong Yuan Yuh, Elangovan Kabilan, Ting Daniel, Chan Anita Sookyee, Shen Sunny
Аннотация
PURPOSE: To develop and validate a deep learning model for the detection of functional blepharoptosis from external ocular photographs, and to quantify the impact of augmenting the training data with synthetic images on model performance.
METHODS: External ocular photographs of 771 eyes from patients aged ≥ 21 years seen at a tertiary oculoplastic clinic. including 639 with clinically diagnosed functional blepharoptosis and 132 without, were obtained and cropped. These were then randomly assigned into training (n = 539), validation (n = 76) and test (n = 156) subsets, to train and evaluate a baseline deep learning model. Additional synthetic data from a pretrained StyleGAN model was then used to augment the training set (n = 2000), to train and evaluate an augmented deep learning model. Analysis of the performance of both models was then performed.
RESULTS: Accuracy of the deep learning models was assessed in terms of sensitivity and specificity in identifying eye images with functionally significant blepharoptosis. A sensitivity of 0.68 (0.60-0.76), specificity of 0.89 (0.77-1.00) and AUC of 0.87 (0.81-0.93) was obtained by the baseline model, and a sensitivity of 0.95 (0.92-0.99), specificity of 0.67 (0.49-0.84) and AUC of 0.91 (0.86-0.96) by the GAN augmented model.
CONCLUSIONS: Functional blepharoptosis can be detected from external ocular photographs with high confidence, and the use of synthetic data from generative models has the potential to further improve the model performance.
METHODS: External ocular photographs of 771 eyes from patients aged ≥ 21 years seen at a tertiary oculoplastic clinic. including 639 with clinically diagnosed functional blepharoptosis and 132 without, were obtained and cropped. These were then randomly assigned into training (n = 539), validation (n = 76) and test (n = 156) subsets, to train and evaluate a baseline deep learning model. Additional synthetic data from a pretrained StyleGAN model was then used to augment the training set (n = 2000), to train and evaluate an augmented deep learning model. Analysis of the performance of both models was then performed.
RESULTS: Accuracy of the deep learning models was assessed in terms of sensitivity and specificity in identifying eye images with functionally significant blepharoptosis. A sensitivity of 0.68 (0.60-0.76), specificity of 0.89 (0.77-1.00) and AUC of 0.87 (0.81-0.93) was obtained by the baseline model, and a sensitivity of 0.95 (0.92-0.99), specificity of 0.67 (0.49-0.84) and AUC of 0.91 (0.86-0.96) by the GAN augmented model.
CONCLUSIONS: Functional blepharoptosis can be detected from external ocular photographs with high confidence, and the use of synthetic data from generative models has the potential to further improve the model performance.