Open Access
ARTICLE
Surgical Outcome Prediction in Total Knee Arthroplasty Using Machine Learning
Belayat Hossaina, Takatoshi Morookab, Makiko Okunob, Manabu Niia, Shinichi Yoshiyab, Syoji Kobashia
aGraduate School of Engineering, University of Hyogo, Hyogo, Japan
bDepartment of Orthopaedics Surgery, Hyogo College of Medicine, Hyogo, Japan
* Corresponding Author: Belayat Hossain,
Intelligent Automation & Soft Computing 2019, 25(1), 105-115. https://doi.org/10.31209/2018.100000034
Abstract
This work aimed to predict postoperative knee functions of a new patient prior to
total knee arthroplasty (TKA) surgery using machine learning, because such
prediction is essential for surgical planning and for patients to better understand
the TKA outcome. However, the main difficulty is to determine the relationships
among individual varieties of preoperative and postoperative knee kinematics.
The problem was solved by constructing predictive models from the knee
kinematics data of 35 osteoarthritis patients, operated by posterior stabilized
implant, based on generalized linear regression (GLR) analysis. Two prediction
methods (without and with principal component analysis followed by GLR) along
with their sub-classes were proposed, and they were finally evaluated by a leaveone-out cross-validation procedure. The best method can predict the
postoperative outcome of a new patient with a Pearson’s correlation coefficient
(cc) of 0.84±0.15 (mean±SD) and a root-mean-squared-error (RMSE) of
3.27±1.42 mm for anterior-posterior vs. flexion/extension (
A-P pattern), and a cc
of 0.89±0.15 and RMSE of 4.25±1.92° for valgus-varus vs. flexion/extension (
i-e
pattern). Although these were validated for one type of prosthesis, they could be
applicable to other implants, because the definition of knee kinematics, measured
by a navigation system, is appropriate for other implants.
Keywords
Cite This Article
B. Hossain, T. Morooka, M. Okuno, M. Nii, S. Yoshiya
et al., "Surgical outcome prediction in total knee arthroplasty using machine learning,"
Intelligent Automation & Soft Computing, vol. 25, no.1, pp. 105–115, 2019. https://doi.org/10.31209/2018.100000034