Application of Deep Learning Algorithm in the Grading Assessment of Corneal Fluorescein Staining

ZHAO Yuxuan, ZHANG Xiaoyun, YANG Bi, LIU Longqian

Abstract

To explore the application value of applying deep learning (DL) algorithm in the grading assessment of corneal fluorescein staining.   Methods  A cross-sectional study was carried out, covering 600 corneal fluorescein staining photos acquired in the Contact Lens Clinic, West China Hospital, Sichuan University between 2020 and 2022. Out of the 600 photos, 500 were used to construct the algorithm and the remaining 100 were used for the validation of the algorithm and a comparative analysis of the difference in grading accuracy (ACC) and the length of diagnostic time between artificial intelligence (AI) and optometry students. One month after finishing the first grading analysis, assessment by AI and optometry students was conducted for a second time and results from the two rounds of assessment were compared to examine the intrarater agreement (kappa value) of the two analyses. The grading analysis results of 3 experienced optometrists were used as the gold standard in the study.   Results   Findings of the cross validation with the complete dataset, the training dataset, and the test dataset showed that ResNet34 had the highest predictive accuracy among four DL models. ResNet34 DL model achieved an accuracy of 93.0%, sensitivity of 89.5%, and specificity of 89.6% in the grading of corneal staining. In the comparison of the grading accuracy of AI and two optometry students, AI showed better accuracy, with the respective grading accuracy being 87.0%, 78.0%, and 52.0% for AI, student 1, and student 2 (PACC=0.001). In addition, the average diagnostic time of AI was shorter than that of optometry students (tAI=1.00 s, tS1=11.86 s, tS2=13.25 s, Pt=0.001). In the comparative analysis of the intrarater agreement between the two assessments, AI (kappaAI=0.658, PAI=0.001) achieved better consistency than the two optometry students did （kappaS1=0.575, PS1=0.001; kappaS2=0.609, PS2=0.001).   Conclusion  Applying deep learning algorithms in the grading assessment of corneal fluorescein staining has considerable feasibility and clinical value. In the performance comparison between AI and optometry students, AI achieved higher accuracy and better consistency, which indicates that AI has potential application value for assisting optometrists to make clinical decisions with speed and accuracy.


Keywords:Artificial intelligence, Contact lens, Corneal staining


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