医学
科克伦图书馆
人工智能
平均绝对误差
科学网
梅德林
机器学习
统计
置信区间
荟萃分析
数学
均方误差
计算机科学
病理
内科学
政治学
法学
作者
Wiktor Stopyra,David L. Cooke,Andrzej E. Grzybowski
摘要
PURPOSE: The proper selection of an intraocular lens power calculation formula is an essential aspect of cataract surgery. This study evaluated the accuracy of artificial intelligence-based formulas. DESIGN: Systematic review. METHODS: This review comprises articles evaluating the exactness of artificial intelligence-based formulas published from 2017 to July 2023. The papers were identified by a literature search of various databases (Pubmed/MEDLINE, Google Scholar, Crossref, Cochrane Library, Web of Science, and SciELO) using the terms "IOL formulas", "FullMonte", "Ladas", "Hill-RBF", "PEARL-DGS", "Kane", "Karmona", "Hoffer QST", and "Nallasamy". In total, 25 peer-reviewed articles in English with the maximum sample and the largest number of compared formulas were examined. RESULTS: The scores of the mean absolute error and percentage of patients within ±0.5 D and ±1.0 D were used to estimate the exactness of the formulas. In most studies the Kane formula obtained the smallest mean absolute error and the highest percentage of patients within ±0.5 D and ±1.0 D. Second place was typically achieved by the PEARL DGS formula. The limitations of the studies were also discussed. CONCLUSIONS: Kane seems to be the most accurate artificial intelligence-based formula. PEARL DGS also gives very good results. Hoffer QST, Karmona, and Nallasamy are the newest, and need further evaluation.
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