Robotics & Machine Learning Daily News2024,Issue(Feb.7) :31-31.DOI:10.3390/jcm13020498

Great Lakes Eye Care Researcher Highlights Research in Artificial Intelligence (A Review of Intraocular Lens Power Calculation Formulas Based on Artificial Intelligence)

Robotics & Machine Learning Daily News2024,Issue(Feb.7) :31-31.DOI:10.3390/jcm13020498

Great Lakes Eye Care Researcher Highlights Research in Artificial Intelligence (A Review of Intraocular Lens Power Calculation Formulas Based on Artificial Intelligence)

扫码查看

Abstract

Investigators publish new report on artificial intelligence. According to news reporting originating from the Great Lakes Eye Care by NewsRx correspondents, research stated, “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.” Our news correspondents obtained a quote from the research from Great Lakes Eye Care: “Systematic review. 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. 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.”

Key words

Great Lakes Eye Care/Artificial Intelligence/Emerging Technologies/Machine Learning

引用本文复制引用

出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
参考文献量61
段落导航相关论文