首页|University Hospital Reports Findings in Artificial Intelligence (Artificial Inte lligence-Guided Assessment of Femoral Neck Fractures in Radiographs: A Systemati c Review and Multilevel Meta-Analysis)
University Hospital Reports Findings in Artificial Intelligence (Artificial Inte lligence-Guided Assessment of Femoral Neck Fractures in Radiographs: A Systemati c Review and Multilevel Meta-Analysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating from Brand enburg an der Havel, Germany, by NewsRx correspondents, research stated, “Artifi cial Intelligence (AI) is a dynamic area of computer science that is constantly expanding its practical benefits in various fields. The aim of this study was to analyze AI-guided radiological assessment of femoral neck fractures by performi ng a systematic review and multilevel meta-analysis of primary studies.” Our news editors obtained a quote from the research from University Hospital, “T he study protocol was registered in the International Prospective Register of Sy stematic Reviews (PROSPERO) on May 21, 2024 [CRD42024541055] . The updated Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were strictly followed. A systematic literature search of P ubMed, Web of Science, Ovid (Med), and Epistemonikos databases was conducted unt il May 31, 2024. Critical appraisal using the Quality Assessment of Diagnostic A ccuracy Studies-2 (QUADAS-2) tool showed that the overall quality of the include d studies was moderate. In addition, publication bias was presented in funnel pl ots. A frequentist multilevel meta-analysis was performed using a random effects model with inverse variance and restricted maximum likelihood heterogeneity est imator with Hartung-Knapp adjustment. The accuracy between AI-based and human as sessment of femoral neck fractures, sensitivity and specificity with 95 % confidence intervals (CIs) were calculated. Study heterogeneity was assessed usi ng the Higgins test I (low heterogeneity <25%, moderate heterogeneity: 25%-75%, and high heterogenei ty >75%). Finally, 11 studies with a total of 21,163 radiographs were included for meta-analysis. The results of the study quality assessment using the QUADAS-2 tool are presented in Table 2. The funnel plots indicated a moderate publication bias. The AI showed excellent accuracy in assessment of femoral neck fractures (Accuracy = 0.91, 95% CI 0.8 3 to 0.96; I = 99%; p<0.01). The AI showed go od sensitivity in assessment of femoral neck fractures (Sensitivity = 0.87, 95% CI 0.77 to 0.93; I = 98%; p<0.01). The AI sho wed excellent specificity in assessment of femoral neck fractures (Specificity = 0.91, 95% CI 0.77 to 0.97; I = 97%; p<0.01). AI-guided radiological assessment of femoral neck fractures showed excel lent accuracy and specificity as well as good sensitivity.”
Brandenburg an der HavelGermanyEurop eArtificial IntelligenceEmerging TechnologiesFemoral Neck FractureHealth and MedicineMachine Learning
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