首页|Xi’an Jiaotong University Reports Findings in Machine Learning (Machine learning value in the diagnosis of vertebral fractures: A systematic review and meta-ana lysis)
Xi’an Jiaotong University Reports Findings in Machine Learning (Machine learning value in the diagnosis of vertebral fractures: A systematic review and meta-ana lysis)
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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 from Xi’an, People’s Republic of China, by NewsRx journalists, research stated, “To evaluate the diagnostic a ccuracy of machine learning (ML) in detecting vertebral fractures, considering v arying fracture classifications, patient populations, and imaging approaches. A systematic review and meta-analysis were conducted by searching PubMed, Embase, Cochrane Library, and Web of Science up to December 31, 2023, for studies using ML for vertebral fracture diagnosis.” The news correspondents obtained a quote from the research from Xi’an Jiaotong U niversity, “Bias risk was assessed using QUADAS-2. A bivariate mixed-effects mod el was used for the meta-analysis. Metaanalyses were performed according to fiv e task types (vertebral fractures, osteoporotic vertebral fractures, differentia tion of benign and malignant vertebral fractures, differentiation of acute and c hronic vertebral fractures, and prediction of vertebral fractures). Subgroup ana lyses were conducted by different ML models (including ML and DL) and modeling m ethods (including CT, X-ray, MRI, and clinical features). Eighty-one studies wer e included. ML demonstrated a diagnostic sensitivity of 0.91 and specificity of 0.95 for vertebral fractures. Subgroup analysis showed that DL (SROC 0.98) and C T (SROC 0.98) performed best overall. For osteoporotic fractures, ML showed a se nsitivity of 0.93 and specificity of 0.96, with DL (SROC 0.99) and X-ray (SROC 0 .99) performing better. For differentiating benign from malignant fractures, ML achieved a sensitivity of 0.92 and specificity of 0.93, with DL (SROC 0.96) and MRI (SROC 0.97) performing best. For differentiating acute from chronic vertebra l fractures, ML showed a sensitivity of 0.92 and specificity of 0.93, with ML (S ROC 0.96) and CT (SROC 0.97) performing best. For predicting vertebral fractures , ML had a sensitivity of 0.76 and specificity of 0.87, with ML (SROC 0.80) and clinical features (SROC 0.86) performing better. ML, especially DL models applie d to CT, MRI, and X-ray, shows high diagnostic accuracy for vertebral fractures. ML also effectively predicts osteoporotic vertebral fractures, aiding in tailor ed prevention strategies.”
Xi’anPeople’s Republic of ChinaAsiaCyborgsDiagnostics and ScreeningEmerging TechnologiesHealth and MedicineMachine Learning
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