Development and validation of a deep learning model to predict axial length from ultra-wide field images

BACKGROUND: To validate the feasibility of building a deep learning model to predict axial length (AL) for moderate to high myopic patients from ultra-wide field (UWF) images. METHODS: This study included 6174 UWF images from 3134 myopic patients during 2014 to 2020 in Eye and ENT Hospital of Fudan University. Of 6174 images, 4939 were used for training, 617 for validation, and 618 for testing. The coefficient of determination (R-2), mean absolute error (MAE), and mean squared error (MSE) were used for model performance evaluation. RESULTS: The model predicted AL with high accuracy. Evaluating performance of R-2, MSE and MAE were 0.579, 1.419 and 0.9043, respectively. Prediction bias of 64.88% of the tests was under 1-mm error, 76.90% of tests was within the range of 5% error and 97.57% within 10% error. The prediction bias had a strong negative correlation with true AL values and showed significant difference between male and female (P < 0.001). Generated heatmaps demonstrated that the model focused on posterior atrophy changes in pathological fundus and peri-optic zone in normal fundus. In sex-specific models, R-2, MSE, and MAE results of the female AL model were 0.411, 1.357, and 0.911 in female dataset and 0.343, 2.428, and 1.264 in male dataset. The corresponding metrics of male AL models were 0.216, 2.900, and 1.352 in male dataset and 0.083, 2.112, and 1.154 in female dataset. CONCLUSIONS: It is feasible to utilize deep learning models to predict AL for moderate to high myopic patients with UWF images.

Automated summary

This study validates the feasibility of using a deep learning model to predict axial length for myopic patients from ultra-wide field images, and demonstrates that the model predicts with high accuracy.