首页|Cantonal Hospital Zenica Researchers Provide New Insights into Machine Learning (Sensitivity and specificity of machine learning and deep learning algorithms in the diagnosis of thoracolumbar injuries resulting in vertebral fractures: A ... )
Cantonal Hospital Zenica Researchers Provide New Insights into Machine Learning (Sensitivity and specificity of machine learning and deep learning algorithms in the diagnosis of thoracolumbar injuries resulting in vertebral fractures: A ... )
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New study results on artificial intelligence have been published. According to news originating from the Cantonal Hospital Zenica by NewsRx correspondents, research stated, “Clinicians encounter challenges in promptly diagnosing thoracolumbar injuries (TLIs) and fractures (VFs), motivatin g the exploration of Artificial Intelligence (AI) and Machine Learning (ML) and Deep Learning (DL) technologies to enhance diagnostic capabilities. Despite vary ing evidence, the noteworthy transformative potential of AI in healthcare, lever aging insights from daily healthcare data, persists.” The news journalists obtained a quote from the research from Cantonal Hospital Z enica: “Research question: This review investigates the utilization of ML and DL in TLIs causing VFs. Employing Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) methodology, a systematic review was conducted in Pu bMed and Scopus databases, identifying 793 studies. Seventeen were included in t he systematic review, and 11 in the meta-analysis. Variables considered encompas sed publication years, geographical location, study design, total participants ( 14,524), gender distribution, ML or DL methods, specific pathology, diagnostic m odality, test analysis variables, validation details, and key study conclusions. Meta-analysis assessed specificity, sensitivity, and conducted hierarchical sum mary receiver operating characteristic curve (HSROC) analysis. Predominantly con ducted in China (29.41%), the studies involved 14,524 participants. In the analysis, 11.76% (N = 2) focused on ML, while 88.24% (N = 15) were dedicated to deep DL. Meta-analysis revealed a sensitivity of 0.91 (95% CI = 0.86-0.95), consistent specificity of 0.90 (95% CI = 0.86-0.93), with a false positive rate of 0.097 (95% CI = 0.0 68-0.137).”
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