Kinetics prediction of normal knee and undergone total knee arthroplasty during squatting based on extreme gradient boosting - Chaiyasit, Kasiwat; Phombut, Chotchuang; Wiangkham, Attasit; Rooppakhun, Supakit (Results in engineering, 2024)
Following an arthroplasty, biomechanical evaluation of the knee joint is crucial because it plays a significant role in patient rehabilitation and physician follow-up. The sixty-two subjects, consisting of 31 with normal knees and 31 who had undergone total knee arthroplasty, evaluated the kinetics of the knee joints during squatting using inverse dynamics analysis. Artificial intelligence based on the XGBoost model was also used to predict kinetics data during group squatting. The result revealed that the maximum force, moment, and quadriceps force in the normal knee were higher than in the TKA group during squatting. The maximum resultant joint force magnitude in the normal and TKA groups revealed an average of 4.35 ± 1.00 and 3.08 ± 1.20 times body weight, respectively. Kinetics of the knee joint in a normal knee significantly differed (p < 0.05) from undergoing TKA, especially in the AP-direction force. Additionally, the resultant moment that included the quadriceps muscle force in the normal group was higher than in the TKA groups. The extreme gradient boosting algorithm (XGBoost) model has prediction performance metrics, which are considered to have high correlation performance in the case of kinetics. •The kinetics of knee joints during squatting posture in the undergoing total knee arthroplasty group and the normal group were predicted based on machine learning.•Using the inverse dynamics method, the kinetics of the knee joint in a normal knee significantly differed (p < 0.05) from undergoing TKA.•Artificial intelligence prediction models showed high performance when applied in the preliminary prediction of knee kinematics.