TY - EJOU
AU - Hossain, Belayat
AU - Morooka, Takatoshi
AU - Okuno, Makiko
AU - Nii, Manabu
AU - Yoshiya, Shinichi
AU - Kobashi, Syoji
TI - Surgical Outcome Prediction in Total Knee Arthroplasty Using Machine Learning
T2 - Intelligent Automation \& Soft Computing
PY - 2019
VL - 25
IS - 1
SN - 2326-005X
AB - 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.
KW - Total knee arthroplasty
KW - knee implant
KW - knee kinematics
KW - machine learning
KW - generalized linear regression
KW - prediction
DO - 10.31209/2018.100000034