Prediction of Axial Length From Macular Optical Coherence Tomography Using Deep Learning Model.

PURPOSE

The purpose of this study was to develop a deep learning model for predicting the axial length (AL) of eyes using optical coherence tomography (OCT) images.

METHODS

We retrospectively included patients with AL measurements and OCT images taken within 3 months. We utilized a 5-fold cross-validation with the ResNet-152 architecture, incorporating horizontal OCT images, vertical OCT images, and dual-input images. The mean absolute error (MAE), R-squared (R2), and the percentages of eyes within error ranges of ±1.0, ±2.0, and ±3.0 mm were calculated.

RESULTS

A total of 9064 eyes of 5349 patients (total image number of 18,128) were included. The average AL of the eyes was 24.35 ± 2.03 (range = 20.53-37.07). Utilizing horizontal and vertical OCT images as dual inputs, deep learning models predicted AL with MAE and R2 of 0.592 mm and 0.847 mm, respectively, in the internal test set (1824 eyes of 1070 patients). In the external test set (171 eyes of 123 patients), the deep learning models predicted AL with MAE and R2 of 0.556 mm and 0.663 mm, respectively. Regarding error margins of ±1.0, ±2.0, and ±3.0 mm, the dual-input models showed accuracies of 83.50%, 98.14%, and 99.45%, respectively, in the internal test set, and 85.38%, 99.42%, and 100.00%, respectively, in the external test set.

CONCLUSIONS

A deep learning-based model accurately predicts AL from OCT images. The dual-input model showed the best performance, demonstrating the potential of macular OCT images in AL prediction.

TRANSLATIONAL RELEVANCE

The study provides new insights into the relationship between retinal and choroidal structures and AL elongation using artificial intelligence models.