Citation: Shokrollahi, Y.; Dong, P.;
Kaya, M.; Suh, D.W.; Gu, L. Rapid
Prediction of Retina Stress and Strain
Patterns in Soccer-Related Ocular
Injury: Integrating Finite Element
Analysis with Machine Learning
Approach. Diagnostics 2022, 12, 1530.
https://doi.org/10.3390/
diagnostics12071530
Academic Editors: Keun Ho Ryu and
Nipon Theera-Umpon
Received: 18 April 2022
Accepted: 20 June 2022
Published: 23 June 2022
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Article
Rapid Prediction of Retina Stress and Strain Patterns in
Soccer-Related Ocular Injury: Integrating Finite Element
Analysis with Machine Learning Approach
Yasin Shokrollahi
1
, Pengfei Dong
1
, Mehmet Kaya
1
, Donny W. Suh
2
and Linxia Gu
1,
*
1
Department of Biomedical and Chemical Engineering and Sciences, Florida Institute of Technology,
Melbourne, FL 32901, USA; yshokrollahi2020@my.fit.edu (Y.S.); pdong@fit.edu (P.D.); mkaya@fit.edu (M.K.)
2
Gavin Herbert Eye Institute (GHEI), University of California at Irvine, Irvine, CA 92697, USA;
donnys@hs.uci.edu
* Correspondence: gul@fit.edu
Abstract:
Soccer-related ocular injuries, especially retinal injuries, have attracted increasing attention.
The mechanics of a flying soccer ball have induced abnormally higher retinal stresses and strains,
and their correlation with retinal injuries has been characterized using the finite element (FE) method.
However, FE simulations demand solid mechanical expertise and extensive computational time, both
of which are difficult to adopt in clinical settings. This study proposes a framework that combines FE
analysis with a machine learning (ML) approach for the fast prediction of retina mechanics. Different
impact scenarios were simulated using the FE method to obtain the von Mises stress map and the
maximum principal strain map in the posterior retina. These stress and strain patterns, along with
their input parameters, were used to train and test a partial least squares regression (PLSR) model to
predict the soccer-induced retina stress and strain in terms of distributions and peak magnitudes.
The peak von Mises stress and maximum principal strain prediction errors were 3.03% and 9.94%
for the frontal impact and were 9.08% and 16.40% for the diagonal impact, respectively. The average
prediction error of von Mises stress and the maximum principal strain were 15.62% and 21.15% for
frontal impacts and were 10.77% and 21.78% for diagonal impacts, respectively. This work provides
a surrogate model of FE analysis for the fast prediction of the dynamic mechanics of the retina in
response to the soccer impact, which could be further utilized for developing a diagnostic tool for
soccer-related ocular trauma.
Keywords:
soccer-related ocular injuries; finite element analysis; retinal stress; retinal strain; machine
learning; partial least squares regression
1. Introduction
Soccer is the fastest growing youth sport, and soccer-related ocular injuries in young
players have attracted increasing attention [
1
]. A fast-moving soccer ball hitting the eye
could cause hyphemia, corneal abrasions, traumatic retinal edema, retinal hemorrhage,
retinal detachment, macular hole, choroidal hemorrhages, and even impaired vision [
2
–
12
].
Horn et al. [
11
] reported thirteen soccer-related retinal injury cases, with half of these cases
requiring surgical intervention. Filipe et al. [
5
] reported severe ocular injuries in soccer
players at all skill levels, sometimes without early symptoms. Retinal hemorrhages on the
posterior segment of the eye are a common ocular injury in youth soccer players [
13
]. It is
critical to quantify the flying soccer ball-induced forces on the eyeball to better understand
the mechanism of soccer-related ocular injuries.
The finite element (FE) method has been a popular tool for quantifying the physics
and mechanics of lesions and providing insights into injuries. Weaver et al. characterized
the ocular lesion’s stresses, energy, and pressures associated with the risk of injury [
14
].
Stress and strain distributions were also obtained from FE models in abuse head trauma
Diagnostics 2022, 12, 1530. https://doi.org/10.3390/diagnostics12071530 https://www.mdpi.com/journal/diagnostics
