Predicting the Shape of Corneas from Clinical Data with Machine Learning Models

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2024-08-21 DOI:10.1016/j.irbm.2024.100853
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引用次数: 0

Abstract

Objective

In ophthalmology, there is a need to explore the relationships between clinical parameters of the cornea and the corneal shape. This study explores the paradigm of machine learning with nonlinear regression methods to verify whether corneal shapes can effectively be predicted from clinical data only, in an attempt to better assess and visualize their effects on the corneal shape.

Methods

The dimensionality of a database of normal anterior corneal surfaces was first reduced by Zernike modeling into short vectors of 12 to 20 coefficients used as targets. The associated structural, refractive and demographic corneal parameters were used as predictors. The nonlinear regression methods were borrowed from the scikit-learn library. All possible regression models (method + predictors + targets) were pre-tested in an exploratory step and those that performed better than linear regression were fully tested with 10-fold validation. The best model was selected based on mean RMSE scores measuring the distance between the predicted corneal surfaces of a model and the raw (non-modeled) true surfaces. The quality of the best model's predictions was visually assessed thanks to atlases of average elevation maps that displayed the centroids of the predicted and true surfaces on a number of clinical variables.

Results

The best model identified was gradient boosting regression using all available clinical parameters to predict 16 Zernike coefficients. The predicted and true corneal surfaces represented in average elevation maps were remarkably similar. The most explicative predictor was the radius of the best-fit sphere, and departures from that sphere were mostly explained by the eye side and by refractive parameters (axis and cylinder).

Conclusion

It is possible to make a reasonably good prediction of the normal corneal shape solely from a set of clinical parameters. In so doing, one can visualize their effects on the corneal shape and identify its most important contributors.

Abstract Image

利用机器学习模型从临床数据中预测角膜形状
目的在眼科领域,需要探索角膜临床参数与角膜形状之间的关系。本研究利用非线性回归方法探索机器学习的范例,以验证是否能仅根据临床数据有效预测角膜形状,从而更好地评估和直观显示临床数据对角膜形状的影响。方法首先通过 Zernike 建模将正常角膜前表面数据库的维度降低为 12 到 20 个系数的短向量作为目标。相关的结构、屈光和人口统计学角膜参数被用作预测因子。非线性回归方法借用了 scikit-learn 库。在探索步骤中对所有可能的回归模型(方法 + 预测因子 + 目标)进行了预先测试,并对性能优于线性回归的模型进行了 10 倍验证的全面测试。根据测量模型预测角膜表面与原始(非建模)真实表面之间距离的平均 RMSE 分数,选出最佳模型。最佳模型的预测质量可通过平均高程图来直观评估,平均高程图显示了预测角膜表面和真实角膜表面在一些临床变量上的中心点。平均高程图所代表的预测角膜表面和真实角膜表面非常相似。最能说明问题的预测因子是最佳拟合球面的半径,而偏离该球面的情况主要由眼侧和屈光参数(轴和圆柱)来解释。通过这种方法,我们可以直观地看到这些参数对角膜形状的影响,并找出最重要的影响因素。
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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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