首页|Studies from All India Institute of Medical Sciences (AIIMS) in the Area of Arti ficial Intelligence Reported (Diagnostic Accuracy of Artificial Intelligence-bas ed Algorithms In Automated Detection of Neck of Femur Fracture On a Plain ...)
Studies from All India Institute of Medical Sciences (AIIMS) in the Area of Arti ficial Intelligence Reported (Diagnostic Accuracy of Artificial Intelligence-bas ed Algorithms In Automated Detection of Neck of Femur Fracture On a Plain ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Data detailed on Artificial Intelligence have bee n presented. According to news reporting originating from Jharkhand, India, by N ewsRx correspondents, research stated, “To evaluate the diagnostic accuracy of a rtificial intelligence-based algorithms in identifying neck of femur fracture on a plain radiograph. Systematic review and meta-analysis.” Our news editors obtained a quote from the research from the All India Institute of Medical Sciences (AIIMS), “PubMed, Web of science, Scopus, IEEE, and the Sci ence direct databases were searched from inception to 30 July 2023. Eligibility criteria for study selection Eligible article types were descriptive, analytical , or trial studies published in the English language providing data on the utili ty of artificial intelligence (AI) based algorithms in the detection of the neck of the femur (NOF) fracture on plain X-ray. The prespecified primary outcome wa s to calculate the sensitivity, specificity, accuracy, Youden index, and positiv e and negative likelihood ratios. Two teams of reviewers (each consisting of two members) extracted the data from available information in each study. The risk of bias was assessed using a mix of the CL(the Checklist for AI in Medical Imagi ng) and QUADAS-2 (A Revised Tool for the Quality Assessment of Diagnostic Accura cy Studies) criteria. Of the 437 articles retrieved, five were eligible for incl usion, and the pooled sensitivity of AIs in diagnosing the fracture NOF was 85% , with a specificity of 87%. For all studies, the pooled Youden ind ex (YI) was 0.73. The average positive likelihood ratio (PLR) was 19.88, whereas the negative likelihood ratio (NLR) was 0.17. The random effects model showed a n overall odds of 1.16 (0.84-1.61) in the forest plot, comparing the AI system w ith those of human diagnosis. The overall heterogeneity of the studies was margi nal (I-2 = 51%). The CLcriteria for risk of bias assessment had an overall >70% score.”
JharkhandIndiaAsiaAlgorithmsArti ficial IntelligenceEmerging TechnologiesMachine LearningAll India Institut e of Medical Sciences (AIIMS)
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