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A deep learning algorithm analyzing one fundus photograph can predict progression to moderate or high myopia, according to a study presented at the annual meeting of the American Academy of Ophthalmology, held Oct. 9 to 12 in New Orleans.

Ang Ji, M.B.B.S., and Yin Guo, M.D., from Beijing Tongren Eye Center in China, developed and validated algorithms to predict one-year myopia onset and progression to moderate myopia and high myopia using nonmydriatic fundus imaging.

A two-stage transfer learning approach was pretrained on 150,213 cross-sectional images; fine-tuning/internal validation of models was performed on a longitudinal cohort of 16,384 children, and external validation was performed on two other cohorts (external validation cohort 1 [EC1], 2,309 participants; external validation cohort 2 [EC2], 2,783 participants).

The researchers found that for predicting progression to moderate myopia and high myopia, the algorithms demonstrated robust performance (area under the receiver operating characteristic curves: 0.89, 0.89, and 0.88 for internal, EC1, and EC2, respectively, for moderate myopia; 0.95, 0.96, and 0.91 for internal, EC1, and EC2, respectively, for high myopia). For myopia onset, prediction was moderate (area under the receiver operating characteristic curve: 0.73, 0.68, and 0.72 for internal, EC1, and EC2, respectively).

"Deep learning using a single fundus photograph can effectively stratify the one-year risk of progression to moderate and high myopia, offering a powerful tool for early clinical intervention," the authors write.

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