首页|Peking University Third Hospital Reports Findings in Osteonecrosis (Machine lear ning models to predict osteonecrosis in patients with femoral neck fractures und ergoing internal fixation)
Peking University Third Hospital Reports Findings in Osteonecrosis (Machine lear ning models to predict osteonecrosis in patients with femoral neck fractures und ergoing internal fixation)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Musculoskeletal Diseas es and Conditions - Osteonecrosis is the subject of a report. According to news reporting from Beijing, People’s Republic of China, by NewsRx journalists, resea rch stated, “This study aimed to use machine learning (ML) to establish risk fac tor and prediction models of osteonecrosis of the femoral head (ONFH) in patient s with femoral neck fractures (FNFs) after internal fixation. We retrospectively collected clinical data of patients with FNFs who were followed up for at least 2 years.” The news correspondents obtained a quote from the research from Peking Universit y Third Hospital, “Only intracapsular FNFs were included. In total, 437 patients and 24 variables were enrolled. The entire dataset was divided into training (8 9.5 %) and test (10.5 %) datasets. Six models-logistic regression, naive Bayes, decision tree, random forest, multilayer perceptron, a nd AdaBoost-were established and validated for predicting postoperative ONFH. We compared the area under the receiver operating characteristic curve (AUC), accu racy, recall, and F1 score of different models. In addition, a confusion matrix, density curve, and learning curve were used to evaluate the model performance. The logistic regression model performed best at predicting ONFH in patients with FNFs undergoing internal fixation surgery, with an AUC, accuracy, recall, F1 sc ore, and prediction value of 0.84, 0.89, 1.00, 0.94, and 89.1 %, re spectively. The learning and density curves demonstrated a good prediction fitti ng degree and distinct separation. When establishing the ML models, the reductio n quality, internal fixation removal, American Society of Anesthesiologists clas sification, injury mechanism, and displacement distance of the medial cortex wer e the top five risk factors positively correlated with the occurrence of ONFH. T he logistic regression model had excellent performance in predicting ONFH in pat ients with FNFs after internal fixation and could provide valuable guidance in c linical decision-making.”
BeijingPeople’s Republic of ChinaAsi aBone Diseases and ConditionsCyborgsEmerging TechnologiesFemoral Neck Fr actureHealth and MedicineMachine LearningMusculoskeletal Diseases and Cond itionsOncologyOsteonecrosisRisk and Prevention
NETL
NSTL
