Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jun 28:9:850284.
doi: 10.3389/fmed.2022.850284. eCollection 2022.

Predicting Axial Length From Choroidal Thickness on Optical Coherence Tomography Images With Machine Learning Based Algorithms

Hao-Chun Lu  1   2 , Hsin-Yi Chen  3   4 , Chien-Jung Huang  3 , Pao-Hsien Chu  5 , Lung-Sheng Wu  5 , Chia-Ying Tsai  3   4
Affiliations

Predicting Axial Length From Choroidal Thickness on Optical Coherence Tomography Images With Machine Learning Based Algorithms

Hao-Chun Lu et al. Front Med (Lausanne). .

Abstract

Purpose: We formulated and tested ensemble learning models to classify axial length (AXL) from choroidal thickness (CT) as indicated on fovea-centered, 2D single optical coherence tomography (OCT) images.

Design: Retrospective cross-sectional study.

Participants: We analyzed 710 OCT images from 355 eyes of 188 patients. Each eye had 2 OCT images.

Methods: The CT was estimated from 3 points of each image. We used five machine-learning base algorithms to construct the classifiers. This study trained and validated the models to classify the AXLs eyes based on binary (AXL < or > 26 mm) and multiclass (AXL < 22 mm, between 22 and 26 mm, and > 26 mm) classifications.

Results: No features were redundant or duplicated after an analysis using Pearson's correlation coefficient, LASSO-Pattern search algorithm, and variance inflation factors. Among the positions, CT at the nasal side had the highest correlation with AXL followed by the central area. In binary classification, our classifiers obtained high accuracy, as indicated by accuracy, recall, positive predictive value (PPV), negative predictive value (NPV), F1 score, and area under ROC curve (AUC) values of 94.37, 100, 90.91, 100, 86.67, and 95.61%, respectively. In multiclass classification, our classifiers were also highly accurate, as indicated by accuracy, weighted recall, weighted PPV, weighted NPV, weighted F1 score, and macro AUC of 88.73, 88.73, 91.21, 85.83, 87.42, and 93.42%, respectively.

Conclusions: Our binary and multiclass classifiers classify AXL well from CT, as indicated on OCT images. We demonstrated the effectiveness of the proposed classifiers and provided an assistance tool for physicians.

Keywords: axial length; choroidal thickness; ensemble learning; high myopia; machine learning; optical coherence tomography (OCT).

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Cross-sectional (A) and longitudinal (B) choroidal images from SD-OCT.
FIGURE 2
FIGURE 2
Three positions at which choroid thicknesses was indicated in OCT images.
FIGURE 3
FIGURE 3
Pairwise scatter plots of all features with binary classification.
FIGURE 4
FIGURE 4
Pairwise scatter plots of all features with multiclass classification.
FIGURE 5
FIGURE 5
Flowchart of this study.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Holden BA, Fricke TR, Wilson DA, Jong M, Naidoo KS, Sankaridurg P, et al. Global prevalence of myopia and high myopia and temporal trends from 2000 through 2050. Ophthalmology. (2016) 123:1036–42. - PubMed
    1. Tsai TH, Liu YL, Ma IH, Su CC, Lin CW, Lin LL, et al. Evolution of the prevalence of myopia among Taiwanese schoolchildren: a review of survey data from 1983 through 2017. Ophthalmology. (2021) 128:290–301. 10.1016/j.ophtha.2020.07.017 - DOI - PubMed
    1. Duan F, Yuan Z, Deng J, Wong YL, Yeo AC, Chen X. Choroidal thickness and associated factors among adult myopia: a baseline report from a medical university student cohort. Ophthalmic Epidemiol. (2019) 26:244–50. 10.1080/09286586.2019.1597899 - DOI - PubMed
    1. Tideman JW, Snabel MC, Tedja MS, van Rijn GA, Wong KT, Kuijpers RW, et al. Association of axial length with risk of uncorrectable visual impairment for Europeans with myopia. JAMA Ophthalmol. (2016) 134:1355–63. 10.1001/jamaophthalmol.2016.4009 - DOI - PubMed
    1. Saw SM, Gazzard G, Shih-Yen EC, Chua WH. Myopia and associated pathological complications. Ophthalmic Physiol Opt. (2005) 25:381–91. 10.1111/j.1475-1313.2005.00298.x - DOI - PubMed