Study on Selecting the Surgical methods for Patients with Knee Osteoarthritis based on BP Neural Network
Author(s): Zhang Haohua, Wang Jiaying, Zhang Kuan, Yan Songhua
Knee Osteoarthritis (KOA) seriously affects life quality of patients. Knee arthroplasty, including total knee arthroplasty (TKA) and Unicompartmental Knee Arthroplasty (UKA), is the main and effective treatment method for KOA patients. When selecting the surgical method, the physicians often make decision based on experiences and patient feeling by conducting an interview, KSS score and X-ray test. The diagnosis process is lengthy and subjective. Thus, the aim of this study is to establish a prediction model and explore the methods of selecting surgical way for KOA patients. One hundred and eighty-three patients with moderate and severe KOA were chosen as our subjects. BMI, body weight, age, gender, KSS score and the used surgical way were recorded. X-ray parameters were obtained. Range of Motion (ROM) of knee joint was measured by a wearable motion monitoring system. Back propagation neural network was applied to get the weight of various parameters affecting the selection of surgical methods and a prediction model was established. The results showed that the lateral space of knee joint had the largest relative weight. BMI, age, gender, medial space of knee and ROM had moderate weight; osteophyte or not, weight, and pain scores had less weight. The total accuracy of the established surgical prediction model reached 92.97%. So, when surgeon judges the surgical method, the lateral space of knee is an important parameter. At the same time, the surgeon may combine age, body weight, sex, ROM and medial space. BMI, pain score, and osteophytes or not can just be used as the reference.