首页|Studies from Armed Forces Institute of Dentistry Further Understanding of Artifi cial Intelligence (Comparison of semi and fully automated artificial intelligenc e driven softwares and manual system for cephalometric analysis)
Studies from Armed Forces Institute of Dentistry Further Understanding of Artifi cial Intelligence (Comparison of semi and fully automated artificial intelligenc e driven softwares and manual system for cephalometric analysis)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news reporting originating from the Armed Forces Institute of Dentistry by NewsRx correspondents, research stated, “Cephalometri c analysis has been used as one of the main tools for orthodontic diagnosis and treatment planning. The analysis can be performed manually on acetate tracing sh eets, digitally by manual selection of landmarks or by recently introduced Artif icial Intelligence (AI)-driven tools or softwares that automatically detect land marks and analyze them.” The news journalists obtained a quote from the research from Armed Forces Instit ute of Dentistry: “The use of AI-driven tools is expected to avoid errors and ma ke it less time consuming with effective evaluation and high reproducibility. To conduct intra- and inter-group comparisons of the accuracy and reliability of c ephalometric tracing and evaluation done manually and with AI-driven tools that is WebCeph and CephX softwares. Digital and manual tracing of lateral cephalomet ric radiographs of 54 patients was done. 18 cephalometric parameters were assess ed on each radiograph by 3 methods, manual method and by using semi (WebCeph) an d fully automatic softwares (Ceph X). Each parameter was assessed by two investi gators using these three methods. SPSS was then used to assess the differences i n values of cephalometric variables between investigators, between softwares, be tween human investigator means and software means. ICC and paired T test were us ed for intra-group comparisons while ANOVA and post-hoc were used for inter-grou p comparisons. Twelve out of eighteen variables had high intra-group correlation and significant ICC p-values, 5 variables had relatively lower values and only one variable (SNO) had significantly low ICC value. Fifteen out of eighteen vari ables had minimal detection error using fully-automatic method of cephalometric analysis. Only three variables had lowest detection error using semi-automatic m ethod of cephalometric analysis. Inter-group comparison revealed significant dif ference between three methods for eight variables; Witts, NLA, SNGoGn, Y-Axis, J araback, SNO, MMA and McNamara to Point A.”
Armed Forces Institute of DentistryArt ificial IntelligenceEmerging TechnologiesMachine LearningSoftware
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