首页|New Findings from Catholic University of Korea in the Area of Artificial Intelli gence Published (A Comparative Study of Deep Learning and Manual Methods for Ide ntifying Anatomical Landmarks through Cephalometry and Cone-Beam Computed ...)
New Findings from Catholic University of Korea in the Area of Artificial Intelli gence Published (A Comparative Study of Deep Learning and Manual Methods for Ide ntifying Anatomical Landmarks through Cephalometry and Cone-Beam Computed ...)
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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 new report. According to news reporting out of Seoul, Sout h Korea, by NewsRx editors, research stated, "Researchers have noted that the ad vent of artificial intelligence (AI) heralds a promising era, with potential to significantly enhance diagnostic and predictive abilities in clinical settings. The aim of this meta-analysis is to evaluate the discrepancies in identifying an atomical landmarks between AI and manual approaches." The news reporters obtained a quote from the research from Catholic University o f Korea: "A comprehensive search strategy was employed, incorporating controlled vocabulary (MeSH) and free-text terms. This search was conducted by two reviewe rs to identify published systematic reviews. Three major electronic databases, n amely, Medline via PubMed, the Cochrane database, and Embase, were searched up t o May 2024. Initially, 369 articles were identified. After conducting a comprehe nsive search and applying strict inclusion criteria, atotal of ten studies were deemed eligible for inclusion in the meta-analysis. The results showed that the average difference in detecting anatomical landmarks between artificial intelli gence and manual approaches was 0.35, with a 95% confidence interv al (CI) ranging from -0.09 to 0.78. Additionally, the overall effect between the two groups was found to be insignificant. Upon further analysis of the subgroup of cephalometric radiographs, it was determined that there were no significant differences between the two groups in terms of detecting anatomical landmarks. S imilarly, the subgroup of cone-beam computed tomography (CBCT) revealed no signi ficant differences between the groups."
Catholic University of KoreaSeoulSou th KoreaAsiaArtificial IntelligenceComputed TomographyEmerging Technolog iesImaging TechnologyMachine LearningTechnology
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