The accuracy of intraocular lens power calculation formulas based on artificial intelligence in highly myopic eyes: a systematic review and network meta-analysis - Zhou, Yi; Dai, Minhui; Sun, Lingyu; Tang, Xiangyi; Zhou, Ling; Tang, Zhiyao; Jiang, Jian; Xia, Xiaobo (Frontiers in Public Health, 2023)
Objective: To systematically compare and rank the accuracy of AI-based intraocular lens (IOL) power calculation formulas and traditional IOL formulas in highly myopic eyes. Methods: PubMed, Web of Science, Embase and Cochrane Library for studies published from the inception date to April 2023. The following outcome data were collected: mean absolute error (MAE), percentage of eyes with a refractive prediction error (PE) within ± 0.25, ± 0.50 and ± 1.00 diopters (D), and median absolute error (MedAE). The network meta-analysis was conducted by R 4.3.0 and STATA 17.0. Results: Twelve studies involving 2,430 adult myopic eyes (axial lengths greater than 26.0 mm) which underwent uncomplicated cataract surgery with mono-focal IOL implantation were included. The network meta-analysis of 21 formulas showed that the top three AI-based formulas as per the surface under the cumulative ranking curve (SUCRA) values were XGBoost, Hill-RBF and Kane. The three formulas had the lowest MedAE, and were more accurate than traditional vergence formulas such as SRK/T, Holladay 1, Holladay 2, Haigis and Hoffer Q regarding MAE, percentage of eyes with PE within ± 0.25, ± 0.50 and ± 1.00 D.The top AI-based formulas for calculation IOL power in highly myopic eyes were XGBoost, Hill-RBF and Kane. They were significantly more accurate than traditional vergence formulas, and ranked better than formulas with Wang-Koch AL modifications or newer generations of formulas such as Barrett and Olsen.