Prediction of posterior elevation stability in keratoconus

Xiaosong Han; Yang Shen; Dantong Gu; Xiaoyu Zhang; Ling Sun; Zhi Chen; Xingtao Zhou

doi:10.3389/fbioe.2023.1288134

Prediction of posterior elevation stability in keratoconus

Front Bioeng Biotechnol. 2023 Nov 9:11:1288134. doi: 10.3389/fbioe.2023.1288134. eCollection 2023.

Authors

Xiaosong Han^#^{1

2

3

4}, Yang Shen^#^{1

2

3

4}, Dantong Gu^#⁵, Xiaoyu Zhang^{1

2

3

4}, Ling Sun^{1

2

3

4}, Zhi Chen^{1

2

3

4}, Xingtao Zhou^{1

2

3

4}

Affiliations

¹ Eye Institute and Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.
² NHC Key Laboratory of Myopia, Key Laboratory of Myopia, Fudan University, Chinese Academy of Medical Sciences, Shanghai, China.
³ Shanghai Research Center of Ophthalmology and Optometry, Shanghai, China.
⁴ Shanghai Engineering Research Center of Laser and Autostereoscopic 3D for Vision Care (20DZ2255000), Shanghai, China.
⁵ Clinical Research and Achievement Translation Center, Eye and ENT Hospital, Fudan University, Shanghai, China.

^# Contributed equally.

Abstract

Purpose: This study aimed to investigate the features of progressive keratoconus by means of machine learning. Methods: In total, 163 eyes from 127 patients with at least 3 examination records were enrolled in this study. Pentacam HR was used to measure corneal topography. Steepest meridian keratometry (K₁), flattest meridian keratometry (K₂), steepest anterior keratometry (K_max), central corneal thickness (CCT), thinnest corneal thickness (TCT), anterior radius of cornea (ARC), posterior elevation (PE), index of surface variation (ISV), and index of height deviation (IHD) were input for analysis. Support vector machine (SVM) and logistic regression analysis were applied to construct prediction models. Results: Age, PE, and IHD showed statistically significant differences as the follow-up period extended. K₂, PE, and ARC were selected for model construction. Logistic regression analysis presented a mean area under the curve (AUC) score of 0.780, while SVM presented a mean AUC of 0.659. The prediction sensitivity of SVM was 52.9%, and specificity was 79.0%. Conclusion: It is feasible to use machine learning to predict the progression and prognosis of keratoconus. Posterior elevation exhibits a sensitive prediction effect.

Keywords: Pentacam; cornea; corneal topography; keratoconus; machine learning; posterior elevation.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. National Natural Science Foundation of China (Grant No. 82101183). National Natural Science Foundation of China (Grant No. 81770955). Research Project Grant of Shanghai Municipal Commission of Health and Family Planning (20204Y0058). Project of Shanghai Science and Technology (Grant No. 20410710100). Clinical Research Plan of SHDC (SHDC2020CR1043B). Project of Shanghai Xuhui District Science and Technology (2020-015). Project of Shanghai Xuhui District Science and Technology (XHLHGG202104). Shanghai Engineering Research Center of Laser and Autostereoscopic 3D for Vision Care (20DZ2255000). Construction of a 3D digital intelligent prevention and control platform for the whole life cycle of highly myopic patients in the Yangtze River Delta (21002411600).