Department of Nursing: Physiotherapy and Medicine, University of Almeria, 04120, Almeria, Spain.
Department of Applied Mathematics, University of Almeria, 04120, Almeria, Spain.
Sci Rep. 2024 Oct 8;14(1):23498. doi: 10.1038/s41598-024-74198-7.
The objective is to evaluate the parameters significantly related to calculating the power of the implanted lens and to determine the importance of different biometric, retina, and corneal aberrations variables. A retrospective cross-sectional observational study used a database of 422 patients who underwent cataract surgery at the Oftalvist Center in Almeria between January 2021 and December 2022. A random forest based on machine learning techniques was proposed to classify the importance of preoperative variables for calculating IOL power. Correlations were explored between implanted IOL power and the most important variables in random forests. The importance of each variable was analyzed using the random forest technique, which established a ranking of feature selections based on different criteria. A positive correlation was found with the random forest variables. Selection: axial length (AL), keratometry preoperative, anterior chamber depth (ACD), measured from corneal epithelium to lens, corneal diameter, lens constant, and astigmatism aberration. The variables coma aberration (p-value = 0,12) and macular thickness (p-value = 0,10) were almost slightly significant. In cataract surgery, the implanted IOL power is mainly correlated with axial length, anterior chamber depth, corneal diameter, lens constant, and preoperative keratometry. New variables such as astigmatism and anterior coma aberration and retina variables such as the preoperative central macular thickness could be included in the new generation of biometric formulas based on artificial intelligence techniques.
目的是评估与计算植入人工晶状体的功率相关的参数,并确定不同的生物测量学、视网膜和角膜像差变量的重要性。本回顾性横断面观察性研究使用了一个数据库,其中包含了 422 名于 2021 年 1 月至 2022 年 12 月在阿尔梅里亚 Oftalvist 中心接受白内障手术的患者。提出了一种基于机器学习技术的随机森林来对计算 IOL 功率的术前变量的重要性进行分类。探讨了植入人工晶状体的功率与随机森林中最重要的变量之间的相关性。使用随机森林技术分析了每个变量的重要性,该技术根据不同的标准建立了基于特征选择的排名。随机森林变量之间存在正相关。选择:眼轴(AL)、术前角膜曲率、前房深度(ACD)、从角膜上皮到晶状体的测量值、角膜直径、晶状体常数和散光像差。彗差像差(p 值=0.12)和黄斑厚度(p 值=0.10)变量几乎具有轻微的显著性。在白内障手术中,植入人工晶状体的功率主要与眼轴长度、前房深度、角膜直径、晶状体常数和术前角膜曲率相关。基于人工智能技术的新一代生物测量公式可以纳入新的变量,如散光和前角膜彗差以及视网膜变量,如术前中央黄斑厚度。