关键词:  , 角膜塑形镜；荧光染色；配适状态；深度学习

Abstract:  Objective To develop an automatic and objective quantification algorithm based on fluorescein patterns to evaluate the fitting status of orthokeratology. Design Diagnose test. Participants Ortho-k lens fitting video with fluorescein patterns from 360 subjects during 2022 from Beijing Tongren Hospital. Methods A deep learning algorithm based on an attention mechanism to analyze the fluorescein patterns video was used. The algorithm used key frames of the fluorescein patterns video to capture static morphological information of the ortho-K lens, while the video as a whole was considered comprehensively to obtain dynamic information such as ortho-K lens mobility. The algorithm adopted a two-stage structure, first classifying the tight fitting samples, and based on this result, further classifying the fit and loose fitting samples, the results were compared with the evaluation standard agreed by the 5 experienced optometrists. Main Outcome Measures Sensitivity, diagnosis accuracy, consistency with ophthalmologists’ results. Results In the validation set, our proposed algorithm achieved a classification accuracy of 82%, a sensitivity of 80%, and a specificity of 85% in the first stage of the classification task of fitting tight samples. In the second stage, the model can classify the remaining two types of samples with a correct rate of 88%, a sensitivity of 85% and a specificity of 93%. In the end, the correct rate of classification results for each category could reached more than 80%, which was highly consistent with the judgment given by optometrists. Compared with the results of human evaluation, the results of computer algorithms had a high degree of matching and better repeatability. Conclusions Using the deep learning algorithm based on the attention mechanism, we developed automatic algorithm for automatic analysis of the fluorescein patterns video of orthokeratology, which can make objective judgments about the fitting status of orthokeratology. (Ophthalmol CHN, 2023, 32: 305-309)

Key words:  orthokeratology, fluorescein patterns, fitting status, deep learning