首页|Beijing Jishuitan Hospital Reports Findings in Arthroplasty (Developing a Machin e-Learning Predictive Model for Retention of Posterior Cruciate Ligament in Pati ents Undergoing Total Knee Arthroplasty)
Beijing Jishuitan Hospital Reports Findings in Arthroplasty (Developing a Machin e-Learning Predictive Model for Retention of Posterior Cruciate Ligament in Pati ents Undergoing Total Knee Arthroplasty)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Surgery - Arthroplasty is the subject of a report. According to news reporting originating from Beijin g, People’s Republic of China, by NewsRx correspondents, research stated, “Predi cting whether the posterior cruciate ligament (PCL) should be preserved during t otal knee arthroplasty (TKA) procedures is a complex task in the preoperative ph ase. The choice to either retain or excise the PCL has a substantial effect on t he surgical outcomes and biomechanical integrity of the knee joint after the ope ration.” Our news editors obtained a quote from the research from Beijing Jishuitan Hospi tal, “To enhance surgeons’ ability to predict the removal and retention of the P CL in patients before TKA, we developed machine learning models. We also identif ied significant feature factors that contribute to accurate predictions during t his process. Patients’ data on TKA continuously performed by a single surgeon wh o had intended initially to undergo implantation of cruciate-retaining (CR) pros theses was collected. During the sacrifice of PCL, we utilized anterior-stabiliz ed (AS) tibial bearings. The dataset was split into CR and AS categories to form distinct groups. Relevant information regarding age, gender, body mass index (B MI), the affected side, and preoperative diagnosis was extracted by reviewing th e medical records of the patients. To ensure the authenticity of the research, a n initial step involved capturing X-ray images before the surgery. These images were then analyzed to determine the height of the medial condyle (MMH) and later al condyle (LMH), as well as the ratios between MLW and MMH and MLW and LMH. Add itionally, the insall-salvati index (ISI) was calculated, and the severity of an y varus or valgus deformities was assessed. Eight machine-learning methods were developed to predict the retention of PCL in TKA. Risk factor analysis was perfo rmed using the SHApley Additive exPlanations method. A total of 307 knee joints from 266 patients were included, among which there were 254 females and 53 males . A stratified random sampling technique was used to split patients in a 70:30 r atio into a training dataset and a testing dataset. Eight machine-learning model s were trained using data feeding. Except for the AUC of the LGBM Classifier, wh ich is 0.70, the AUCs of other machine learning models are all lower than 0.70. In importance-based analysis, ISI, MMH, LMH, deformity, and age were confirmed a s important predictive factors for PCL retention in operations. The LGBM Classif ier model achieved the best performance in predicting PCL retention in TKA.”
Beijing, People’s Republic of China, Asi a, Arthroplasty, Cyborgs, Emerging Technologies, Health and Medicine, Knee Arthr oplasty, Machine Learning, Orthopedic Procedures, Risk and Prevention, Surgery
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