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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. Electronic address:
Published: December 2024
AI Article Synopsis
Article Abstract
Purpose: 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.
Methods: 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.
Results: 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.
Conclusions: 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.
