@Article{2018.100000034,
AUTHOR = {Belayat Hossain, Takatoshi Morooka, Makiko Okuno, Manabu Nii, Shinichi Yoshiya, Syoji Kobashi},
TITLE = {Surgical Outcome Prediction in Total Knee Arthroplasty Using Machine  Learning},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {1},
PAGES = {105--115},
URL = {http://www.techscience.com/iasc/v25n1/39636},
ISSN = {2326-005X},
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 (<i>A-P</i> pattern), and a cc 
of 0.89±0.15 and RMSE of 4.25±1.92° for valgus-varus vs. flexion/extension (<i>i-e</i>
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.},
DOI = {10.31209/2018.100000034}
}