Zhou Xueyi, Zeng Li, Shen Yang, Zhang Zhe, Wang Chongyang, Wang Bingjie, Kang Pauline, Zhou Xingtao, Chen Zhi
Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai 200031, China; NHC Key laboratory of Myopia and Related Eye Diseases, Key Laboratory of Myopia and Related Eye Diseases, Chinese Academy of Medical Sciences, Shanghai, 200031, China; Shanghai Research Center of Ophthalmology and Optometry, Shanghai 200031, China; Shanghai Engineering Research Center of Laser and Autostereoscopic 3D for Vision Care (20DZ2255000), China.
MediWorks Precision Instruments Company Limited, Shanghai 200031, China.
Cont Lens Anterior Eye. 2025 Jun;48(3):102350. doi: 10.1016/j.clae.2024.102350. Epub 2024 Dec 16.
Based on ideal outcomes of corneal topography following orthokeratology (OK), an innovative machine learning algorithm for corneal refractive therapy (CRT) was developed to investigate the precision of artificial intelligence (AI)-assisted OK lens fitting.
A total of 797 eyes that had been fitted with CRT lenses and demonstrated good lens centration with plus power ring intact in their topography were retrospectively included. A comprehensive AI model included spherical refraction, keratometry readings, eccentricity, corneal astigmatism, horizontal visible iris diameter, inferior-superior index, surface asymmetry index, surface regularity index and 8-mm chordal corneal height difference. A simplified AI model omitted the latter four parameters. Correlation and disparity in predicted lens parameters between the AI prediction and manufacturer's conventional lens fitting method were compared.
There was overall no significant difference between AI predicted parameters and the final ordered parameters (p > 0.05). The horizontal return zone depth (RZD1, p = 0.022) and vertical return zone depth (RZD2, p < 0.001) values suggested by the conventional method were significantly lower, while the horizontal landing zone angle (LZA1) was significantly larger (p = 0.002) than those of the final ordered lens. The AI predicted parameters were significantly correlated to those of the final ordered lens (p < 0.01), with the correlation coefficients of base curve radius (BCR), RZD1, RZD2, LZA1, vertical LZA (LZA2) and total lens diameter (TD) being 0.958, 0.708, 0.773, 0.697, 0.654 and 0.730, respectively, for the comprehensive AI model. The correlation coefficients were higher in RZD2, LZA1 and TD with the AI model as compared to conventional method.
Compared with the conventional method, AI predicted lens parameters exhibit less disparity and improved accuracy, with a potential to facilitate more efficient and precise CRT OK lens fitting.
基于角膜塑形术（OK）后角膜地形图的理想结果，开发了一种用于角膜屈光治疗（CRT）的创新机器学习算法，以研究人工智能（AI）辅助OK镜片验配的精度。
回顾性纳入797只已佩戴CRT镜片且在地形图中显示镜片中心定位良好且正屈光度环完整的眼睛。一个综合AI模型包括球镜度、角膜曲率读数、偏心率、角膜散光、水平可见虹膜直径、上下指数、表面不对称指数、表面规则性指数和8毫米弦长角膜高度差。一个简化的AI模型省略了后四个参数。比较了AI预测与制造商传统镜片验配方法之间预测镜片参数的相关性和差异。
AI预测参数与最终订购参数之间总体无显著差异（p>0.05）。传统方法建议的水平回退区深度（RZD1，p = 0.022）和垂直回退区深度（RZD2，p<0.001）值显著更低，而水平着陆区角度（LZA1）显著更大（p = 0.002），与最终订购的镜片相比。AI预测参数与最终订购镜片的参数显著相关（p<0.01），综合AI模型的基弧半径（BCR）、RZD1、RZD2、LZA1、垂直LZA（LZA2）和总镜片直径（TD）的相关系数分别为0.958、0.708、0.773、0.697、0.654和0.730。与传统方法相比，AI模型在RZD2、LZA1和TD方面的相关系数更高。
与传统方法相比，AI预测的镜片参数差异更小且准确性更高，有可能促进更高效、精确的CRT OK镜片验配。
