人工智能与传统角膜塑形镜验配方法的比较

IF 4.1 3区 医学 Q1 OPHTHALMOLOGY Contact Lens & Anterior Eye Pub Date : 2024-11-04 DOI:10.1016/j.clae.2024.102321
Jérémy Abadou, Simon Dahan, Juliette Knoeri, Loic Leveziel, Nacim Bouheraoua, Vincent M Borderie
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引用次数: 0

摘要

背景:比较三种人工智能(AI)框架(标准机器学习(ML)、多层感知器(MLP)和卷积神经网络(CNN))与一种参考方法(平均曲率半径(K))预测角膜塑形镜最佳配戴硬性隐形眼镜(RCBFL)后曲率半径的效率:这项回顾性研究纳入了在 2020 年 1 月至 2022 年 9 月期间配戴了 Rose K2® (Menicon®,日本名古屋)硬性隐形眼镜的 135 名患者的 197 只角膜炎眼,这些患者配戴了一种或多种地形图(MS39®,CSO®,意大利费拉拉)。人工智能分析使用了两种地形图数据(指数和从原始数据重建的地图)。人工智能利用了三种不同的方法:基于地形指数的标准 ML 方法和 MLP,以及基于地形图(即角膜厚度图、矢状图和切线图)的 CNN。将 17 个人工智能框架的准确度与预测半径和最佳拟合半径之间线性回归的 r2 决定系数进行了比较。将框架的准确度与皮尔逊相关系数的费舍尔 z 变形进行了比较:在多元线性回归中,只有三个地形指数(即 3 和 5 mm 平均 K 值和 Kmax)与 RCBFL 显著相关(p ≤ 0.0001)。与参考方法(平均 K;r2 = 0.36)相比,使用三种地形指数的随机森林、使用所有指数的 MLP、使用前部地形图的 ResNet18 CNN 和使用组合参数的 CNNs 预测 RCBFL 的效果明显更好(0.69 ≤ r2 ≤ 0.80;p 2 = 0.80):人工智能方法,尤其是使用 MS39® 角膜地形图数据的 CNN,在预测 Rose K2® 硬性角膜接触镜在角膜炎患者中的后曲率半径方面优于传统方法。
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Artificial intelligence versus conventional methods for RGP lens fitting in keratoconus.

Background: To compare the efficiency of three artificial intelligence (AI) frameworks (Standard Machine Learning (ML), Multi-Layer Perceptron (MLP) and Convolution Neural Networks (CNN)) with a reference method (Mean radius of curvature (K)) to predict the posterior radius of curvature of the best-fitted rigid contact lens (RCBFL) in keratoconus eyes.

Methods: This retrospective study included 197 keratoconus eyes of 135 patients fitted with Rose K2® (Menicon®, Nagoya, Japan) rigid contact lenses with one or more topographies available (MS39®, CSO®, Ferrara, Italy) between January 2020 and September 2022. Two types of topographic data (indices and reconstructed maps from raw data) were used for AI analysis. Three distinct approaches were utilized for leveraging AI: Standard ML methods and MLPs based on topographic indices and CNNs based on topographic maps (i.e., corneal thickness, sagittal, and tangential maps). Seventeen AI framework's accuracies were compared with the r2 determination coefficient of linear regression between predicted and best-fitted radii. Framework accuracies were compared with the Fisher z-transformation of Pearson correlation coefficients.

Results: In multiple linear regression, only three topographic indices (i.e., 3- & 5-mm mean K and Kmax) were significantly associated with RCBFL (p ≤ 0.0001). Compared with the reference method (mean-K; r2 = 0.36), a significantly better RCBFL prediction was achieved with Random Forest using the three topographic indices, MLP using all indices, ResNet18 CNN using anterior topographic maps and CNNs using combined parameters (0.69 ≤ r2 ≤ 0.80; p < 0.05). The best accuracy was obtained with the EfficientNetB0 CNN trained with three maps (r2 = 0.80).

Conclusions: Artificial intelligence methods, particularly CNNs, with corneal topography data of MS39® topographer, have demonstrated superiority over conventional approaches in predicting the posterior curvature radius of Rose K2® rigid contact lenses in patients with keratoconus.

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来源期刊
CiteScore
7.60
自引率
18.80%
发文量
198
审稿时长
55 days
期刊介绍: Contact Lens & Anterior Eye is a research-based journal covering all aspects of contact lens theory and practice, including original articles on invention and innovations, as well as the regular features of: Case Reports; Literary Reviews; Editorials; Instrumentation and Techniques and Dates of Professional Meetings.
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