Comparison of Semi and Fully Automated Artificial Intelligence Driven Softwares and Manual System for Cephalometric Analysis

Abstract Background: Cephalometric analysis has been used as one of the main diagnostic tools for orthodontic diagnosis and treatment planning. The analysis can be performed manually on acetate tracing sheets, digitally by manual selection of landmarks or by recently introduced Artificial Intelligence (AI)-driven tools or softwares that automatically detect landmarks and analyze them. The use of AI-driven tools is expected to avoid errors and make it less time consuming with effective evaluation and high reproducibility. Objective: To conduct intra- and inter-group comparisons of the accuracy and reliability of cephalometric tracing and evaluation done manually and with AI-driven tools including WebCeph and CephX softwares. Methods: Digital and manual tracing for cephalometric analyses was conducted for 54 patients. 18 cephalometric parameters were assessed on each radiograph by manual method and by using 2 softwares (Webceph and Ceph X). Each parameter was assessed by two investigators using these three methods. SPSS software was then used to assess the differences in values of cephalometric variables between investigators, between softwares, between human investigator means and software means. ICC and paired T test were used for intra-group comparisons while ANOVA and post-hoc were used for inter-group comparisons. Results: · 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 variables had minimal detection error using fully-automatic method of cephalometric analysis. Only three variables had lowest detection error using semi-automatic method of cephalometric analysis. · Inter-group comparison revealed significant difference between three methods for eight variables; Witts, NLA, SNGoGn, Y-Axis, Jaraback, SNO, MMA and McNamara to Point A. Conclusion: There is a lack of significant difference in the majority of variables among the manual, semi automatic and fully automatic methods of cephalometric tracing and analysis. The mean detection errors were the highest for manual analysis and lowest for fully automatic method. Hence the fully automatic AI software has the most reproducible and accurate results.