Abstract
Purpose
To construct a machine learning (ML)-based model for estimating the alignment curve
(AC) curvature in orthokeratology lens fitting for vision shaping treatment (VST),
which can minimize the number of lens trials, improving efficiency while maintaining
accuracy, with regards to its improvement over a previous calculation method.
Methods
Data were retrospectively collected from the clinical case files of 1271 myopic subjects
(1271 right eyes). The AC curvatures calculated with a previously published algorithm
were used as the target data sets. Four kinds of machine learning algorithms were
implemented in the experimental analyses to predict the targeted AC curvatures: robust
linear regression models, support vector machine (SVM) regression models with linear
kernel functions, bagging decision trees, and Gaussian processes. The previously published
calculation method and the novel machine learning method were then compared to assess
the final parameters of ordered lenses.
Results
The linear SVM and Gaussian process machine learning models achieved the best performance.
The input variables included sex, age, horizontal visible iris diameter (HVID), spherical
refraction (SER), cylindrical refraction, eccentricity value (e value), flat K (K1)
and steep K (K2) readings, anterior chamber depth (ACD), and axial length (AL). The
R-squared values for the output AC1K1, AC1K2 and AC2K1 values were 0.91, 0.84, and
0.73, respectively. The previous calculation method and machine learning methods displayed
excellent consistency, and the proposed methods performed best based on flat K reading
and e values.
Conclusions
The ML model can provide practitioners with an efficient method for estimating the
AC curvatures of VST lenses and reducing the probability of cross-infection originating
from trial lenses, which is especially useful during pandemics, such as that for COVID-19.
Keywords
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Article info
Publication history
Published online: July 20, 2021
Accepted:
June 22,
2021
Received in revised form:
June 14,
2021
Received:
February 2,
2021
Identification
Copyright
© 2021 British Contact Lens Association. Published by Elsevier Ltd. All rights reserved.
