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Fig. 1 | Eye and Vision

Fig. 1

From: A machine learning-based algorithm used to estimate the physiological elongation of ocular axial length in myopic children

Fig. 1

Flow chart of our proposed method. a Data inclusion criteria. b Data processing procedure. c Machine learning models used to predict the axial length and estimate the physiological axial length elongation. The best-performing prediction model was applied to predict the axial length and estimate the physiological axial length elongation by considering the partial derivatives of ALpredicted-age curves. K-mean: mean K reading; CCT: central corneal thickness; ACD: anterior chamber depth; WTW: white-to-white corneal diameter; SER: spherical equivalent refraction error; AL: axial length; SVM: support vector machine; R: the coefficient of determination; MAEs: mean absolute errors; MSEs: mean squared errors; RMSE: root mean square error; N: number of patients

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Eye and Vision

ISSN: 2326-0254