Deep learning-based estimation of axial length using macular optical coherence tomography images

Jing Liu; Hui Li; You Zhou; Yue Zhang; Shuang Song; Xiaoya Gu; Jingjing Xu; Xiaobing Yu

doi:10.3389/fmed.2023.1308923

Deep learning-based estimation of axial length using macular optical coherence tomography images

Front Med (Lausanne). 2023 Nov 17:10:1308923. doi: 10.3389/fmed.2023.1308923. eCollection 2023.

Authors

Jing Liu^{1

2}, Hui Li¹, You Zhou³, Yue Zhang^{1

2}, Shuang Song¹, Xiaoya Gu¹, Jingjing Xu³, Xiaobing Yu^{1

2}

Affiliations

¹ Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
² Graduate School of Peking Union Medical College, Beijing, China.
³ Visionary Intelligence Ltd., Beijing, China.

Abstract

Background: This study aimed to develop deep learning models using macular optical coherence tomography (OCT) images to estimate axial lengths (ALs) in eyes without maculopathy.

Methods: A total of 2,664 macular OCT images from 444 patients' eyes without maculopathy, who visited Beijing Hospital between March 2019 and October 2021, were included. The dataset was divided into training, validation, and testing sets with a ratio of 6:2:2. Three pre-trained models (ResNet 18, ResNet 50, and ViT) were developed for binary classification (AL ≥ 26 mm) and regression task. Ten-fold cross-validation was performed, and Grad-CAM analysis was employed to visualize AL-related macular features. Additionally, retinal thickness measurements were used to predict AL by linear and logistic regression models.

Results: ResNet 50 achieved an accuracy of 0.872 (95% Confidence Interval [CI], 0.840-0.899), with high sensitivity of 0.804 (95% CI, 0.728-0.867) and specificity of 0.895 (95% CI, 0.861-0.923). The mean absolute error for AL prediction was 0.83 mm (95% CI, 0.72-0.95 mm). The best AUC, and accuracy of AL estimation using macular OCT images (0.929, 87.2%) was superior to using retinal thickness measurements alone (0.747, 77.8%). AL-related macular features were on the fovea and adjacent regions.

Conclusion: OCT images can be effectively utilized for estimating AL with good performance via deep learning. The AL-related macular features exhibit a localized pattern in the macula, rather than continuous alterations throughout the entire region. These findings can lay the foundation for future research in the pathogenesis of AL-related maculopathy.

Keywords: Grad-CAM; artificial intelligence; axial length; deep learning; optical coherence tomography.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by National High Level Hospital Clinical Research Funding (BJ-2022-104 and BJ-2020-167).