Evaluation of the corneal topography based on deep learning

Front Med (Lausanne). 2024 Jan 4:10:1264659. doi: 10.3389/fmed.2023.1264659. eCollection 2023.

Abstract

Purpose: The current study designed a unique type of corneal topography evaluation method based on deep learning and traditional image processing algorithms. The type of corneal topography of patients was evaluated through the segmentation of important medical zones and the calculation of relevant medical indicators of orthokeratology (OK) lenses.

Methods: The clinical data of 1,302 myopic subjects was collected retrospectively. A series of neural network-based U-Net was used to segment the pupil and the treatment zone in the corneal topography, and the decentration, effective defocusing contact range, and other indicators were calculated according to the image processing algorithm. The type of corneal topography was evaluated according to the evaluation criteria given by the optometrist. Finally, the method described in this article was used to evaluate the type of corneal topography and compare it with the type classified by the optometrist.

Results: When the important medical zones in the corneal topography were segmented, the precision and recall of the treatment zone reached 0.9587 and 0.9459, respectively, and the precision and recall of the pupil reached 0.9771 and 0.9712. Finally, the method described in this article was used to evaluate the type of corneal topography. When the reviewed findings based on deep learning and image processing algorithms were compared to the type of corneal topography marked by the professional optometrist, they demonstrated high accuracy with more than 98%.

Conclusion: The current study provided an effective and accurate deep learning algorithm to evaluate the type of corneal topography. The deep learning algorithm played an auxiliary role in the OK lens fitting, which could help optometrists select the parameters of OK lenses effectively.

Keywords: corneal topography; deep learning; image processing; orthokeratology lens; treatment zone.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study received funding support from the Science and Technology Project of Tianjin Municipal Health Bureau (No. TJWJ2021MS040), the Tianjin Key Lab of Ophthalmology and Visual Science (No.TJYXZDXK-016A), the Tianjin Municipal Science and Technology Program (No. 22JCQNJC00740), and the Fundamental Research Funds for the Central Universities, Nankai University (63231171).