Deep Learning-based Prediction of Axial Length Using Ultra-widefield Fundus Photography
Richul Oh, Eun Kyoung Lee, Kunho Bae, Un Chul Park, Hyeong Gon Yu, Chang Ki Yoon
Abstract
PURPOSE: To develop a deep learning model that can predict the axial lengths of eyes using ultra-widefield (UWF) fundus photography. METHODS: We retrospectively enrolled patients who visited the ophthalmology clinic at the Seoul National University Hospital between September 2018 and December 2021. Patients with axial length measurements and UWF images taken within 3 months of axial length measurement were included in the study. The dataset was divided into a development set and a test set at an 8:2 ratio while maintaining an equal distribution of axial lengths (stratified splitting with binning). We used transfer learning-based on EfficientNet B3 to develop the model. We evaluated the model's performance using mean absolute error (MAE), R-squared (R2), and 95% confidence intervals (CIs). We used vanilla gradient saliency maps to illustrate the regions predominantly used by convolutional neural network. RESULTS: In total, 8,657 UWF retinal fundus images from 3,829 patients (mean age, 63.98 ±15.25 years) were included in the study. The deep learning model predicted the axial lengths of the test dataset with MAE and R2 values of 0.744 mm (95% CI, 0.709-0.779 mm) and 0.815 (95% CI, 0.785-0.840), respectively. The model's accuracy was 73.7%, 95.9%, and 99.2% in prediction, with error margins of ±1.0, ±2.0, and ±3.0 mm, respectively. CONCLUSIONS: We developed a deep learning-based model for predicting the axial length from UWF images with good performance.