首页|Southeast University Reports Findings in Machine Learning (Automated machine lea rning-based model for the prediction of pedicle screw loosening after degenerati ve lumbar fusion surgery)
Southeast University Reports Findings in Machine Learning (Automated machine lea rning-based model for the prediction of pedicle screw loosening after degenerati ve lumbar fusion surgery)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating from Jiangsu, Peo ple's Republic of China, by NewsRx correspondents, research stated, "The adequac y of screw anchorage is a critical factor in achieving successful spinal fusion. This study aimed to use machine learning algorithms to identify critical variab les and predict pedicle screw loosening after degenerative lumbar fusion surgery ." Our news editors obtained a quote from the research from Southeast University, " A total of 552 patients who underwent primary transpedicular lumbar fixation for lumbar degenerative disease were included. The LASSO method identified key feat ures associated with pedicle screw loosening. Patient clinical characteristics, intraoperative variables, and radiographic parameters were collected and used to construct eight machine learning models, including a training set (80% of participants) and a test set (20 % of participants). The XGBoost model exhibited the best performance, with an AUC of 0.884 (95% C I: 0.825-0.944) in the test set, along with the lowest Brier score. Ten crucial variables, including age, disease diagnosis: degenerative scoliosis, number of f used levels, fixation to S1, HU value, preoperative PT, preoperative PI-LL, post operative LL, postoperative PT, and postoperative PI-LL were selected. In the pr ospective cohort, the XGBoost model demonstrated substantial performance with an accuracy of 83.32%. This study identified crucial variables associ ated with pedicle screw loosening after degenerative lumbar fusion surgery and s uccessfully developed a machine learning model to predict pedicle screw loosenin g."
JiangsuPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesHealth and MedicineMachine LearningSurger y
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