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

ABSTRACT:

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 input

SUBMITTER: Oh R 

PROVIDER: S-EPMC11407480 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature