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

Abstract

Background Axial myopia is the most common type of myopia. However, due to the high incidence of myopia in Chinese children, few studies estimating the physiological elongation of the ocular axial length (AL), which does not cause myopia progression and differs from the non-physiological elongation of AL, have been conducted. The purpose of our study was to construct a machine learning (ML)-based model for estimating the physiological elongation of AL in a sample of Chinese school-aged myopic children. Methods In total, 1011 myopic children aged 6 to 18 years participated in this study. Cross-sectional datasets were used to optimize the ML algorithms. The input variables included age, sex, central corneal thickness (CCT), spherical equivalent refractive error (SER), mean K reading (K-mean), and white-to-white corneal diameter (WTW). The output variable was AL. A 5-fold cross-validation scheme was used to randomly divide all data into 5 groups, including 4 groups used as training data and one group used as validation data. Six types of ML algorithms were implemented in our models. The best-performing algorithm was applied to predict AL, and estimates of the physiological elongation of AL were obtained as the partial derivatives of ALpredicted-age curves based on an unchanged SER value with increasing age. Results Among the six algorithms, the robust linear regression model was the best model for predicting AL, with a R2 value of 0.87 and relatively minimal averaged errors between the predicted AL and true AL. Based on the partial derivatives of the ALpredicted-age curves, the estimated physiological AL elongation varied from 0.010 to 0.116 mm/year in male subjects and 0.003 to 0.110 mm/year in female subjects and was influenced by age, SER and K-mean. According to the model, the physiological elongation of AL linearly decreased with increasing age and was negatively correlated with the SER and the K-mean. Conclusions The physiological elongation of the AL is rarely recorded in clinical data in China. In cases of unavailable clinical data, an ML algorithm could provide practitioners a reasonable model that can be used to estimate the physiological elongation of AL, which is especially useful when monitoring myopia progression in orthokeratology lens wearers.

Development of Refractive Parameters in 3- to 6-Year-Old Children and Its Application in Myopia Prediction and Intervention Guidance

“…Moreover, this device has obtained approval of the Food and Drug Administration (FDA) [24]. However, this kind of screener does reduce its accuracy due to the effect of strong accommodation in younger children, and then, the prevalence of myopia will be overestimated in younger children [10,[25][26][27][28]. The Sankara Nethralaya Tamil Nadu Essilor Myopia (STEM) study [16] pointed out that in noncycloplegic refraction using an open-field autorefractor, the threshold of SE ≤ −0:75 D was equivalent to the threshold value for myopia under cycloplegia (SE ≤ −0:50 D).…”

Correcting magnification error in foveal avascular zone area measurements of optical coherence tomography angiography images with estimated axial length

“…Others have utilised semi-automated computer processing to facilitate AL estimation. Tang et al [ 33 ] compared machine learning with traditional multiple regression formulae in developing a method for estimating AL. Regression models were based on age, gender, K, SE and white-to-white diameter.…”

“…Regression models were based on age, gender, K, SE and white-to-white diameter. Tang et al [ 33 ] found that machine-learning methods outperformed traditional multiple regressions model for estimating AL, with the strongest machine-learning model AL prediction model having a R 2 of 0.86, which is considerably robust, however Tang et al [ 33 ] did not report a mean difference or 95% LoA. A deep-learning algorithm applied to colour fundus photographs was used by Dong et al [ 34 ] to estimate AL, and resulted in R 2 = 0.59 and mean difference of 0.16 mm (95% LoA: − 0.60, 1.27) mm.…”

“…This does not discount the accuracy of the AL est . In comparisons of the magnification error correction performance of AL est with the AL estimation methods presented in the aforementioned studies [13,21,[32][33][34][35], it is important to note that even marginal improvements in the accuracy of estimation methods may provide significant advances in magnification error correction for FAZA, and possibly for other fundus parameters like vessel density.…”

Machine Learning to Determine Risk Factors for Myopia Progression in Primary School Children: The Anyang Childhood Eye Study

“…Machine learning is a method of data analysis that automates analytical model building and has been successfully used in imaging recognition and classification [17]. In the field of ophthalmology, machine learning has been used in diagnosis of diabetic retinopathy [18], predictions of myopia development [19,20], orthokeratology lens prescription [21,22], and visual acuity in patients treated for neovascular age-related macular degeneration [23]. There are many machine learning algorithms, each with its own strengths and weaknesses.…”