Development of machine learning-based models for vault prediction in implantable collamer lens surgery according to implant orientation.

IF 2.6 3区 医学 Q2 OPHTHALMOLOGY
Timoteo González-Cruces, Francisco Javier Aguilar-Salazar, Jordi Marfany Tort, Álvaro Sánchez-Ventosa, Alberto Villarrubia, Jose Lamarca Mateu, Rafael I Barraquer, Sergio Pardina, David Cerdán Palacios, Antonio Cano-Ortiz
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引用次数: 0

Abstract

Purpose: The main objective was to develop a prediction model based on machine learning to calculate the postoperative vault as well as the ideal implantable collamer lens (ICL) size, considering for the first time the implantation orientation in a Caucasian population.

Setting: Arruzafa Ophthalmological Hospital (Cordoba, Spain) and Barraquer Ophthalmology Center (Barcelona, Spain).

Design: Multicenter, randomized, retrospective study.

Methods: Anterior segment biometric data from 235 eyes of patients who underwent ICL lens implantation surgery were collected using the anterior segment optical coherence tomography (AS-OCT) CASIA II, to train and validate five types of multiple regression models based on advanced machine learning techniques. To perform an external validation a dataset of 45 observations was used.

Results: The Pearson correlation coefficient between observed and predicted values was similar in the five models in the external validation, with least absolute shrinkage and selection operator (LASSO) regression being the highest (r = 0.62, p < 0.001), followed by random forest regression model (r = 0.60, p < 0.001) and backward stepwise regression (r = 0.58, ρ < 0.001). In addition, the predictions generated by the different models showed closer agreement with the actual vault compared with the Nakamura formulas. Using the new methods, about 70% of the observations had a prediction error below 150 µm.

Conclusions: Advanced forms of regressions models based on machine learning allow satisfactory calculation of the ideal lens size, offering greater precision to surgeons customizing surgery according to implant orientation.

基于机器学习的基于种植体方向的可植入屈光体手术拱顶预测模型的开发。
目的:主要目的是建立一种基于机器学习的预测模型,以计算高加索人群中植入体的种植方向,以及术后弓形以及理想的植入体collamer lens (ICL)大小。单位:Arruzafa眼科医院(西班牙科尔多瓦)和Barraquer眼科中心(西班牙巴塞罗那)。设计:多中心、随机、回顾性研究。方法:采用前段光学相干断层扫描(AS-OCT) CASIA II采集235只ICL晶状体植入术患者的前段生物特征数据,训练并验证基于先进机器学习技术的5种多元回归模型。为了执行外部验证,使用了45个观测数据集。结果:外部验证的5个模型的观测值与预测值的Pearson相关系数相似,绝对收缩最小,选择算子(LASSO)回归最高(r = 0.62, p < 0.001),随机森林回归模型次之(r = 0.60, p < 0.001),后向逐步回归模型次之(r = 0.58, ρ < 0.001)。此外,与中村公式相比,不同模型产生的预测结果更接近于实际的保险库。使用新方法,约70%的观测值的预测误差低于150µm。结论:基于机器学习的先进形式的回归模型可以令人满意地计算理想的晶状体尺寸,为外科医生根据种植体的方向定制手术提供更高的精度。
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来源期刊
CiteScore
5.60
自引率
14.30%
发文量
259
审稿时长
8.5 weeks
期刊介绍: The Journal of Cataract & Refractive Surgery (JCRS), a preeminent peer-reviewed monthly ophthalmology publication, is the official journal of the American Society of Cataract and Refractive Surgery (ASCRS) and the European Society of Cataract and Refractive Surgeons (ESCRS). JCRS publishes high quality articles on all aspects of anterior segment surgery. In addition to original clinical studies, the journal features a consultation section, practical techniques, important cases, and reviews as well as basic science articles.
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