Is automatic cephalometric software using artificial intelligence better than orthodontist expert in landmark identification

Abstract

Abstract Background: To evaluate the techniques used for automatic digitization of cephalograms, highlighting the strengths and weaknesses of each one and review the percentage of success in localising each cephalometric point.、 Methods: Lateral cephalograms were digitized and traced by three calibrated senior orthodontic residents with or without artificial intelligence (AI) assistance. The same radiographs of 43 patients were uploaded to AI-based machine learning program MyOrthoX, Angelalign and Digident. Image J was used to extract x- and y-coordinates for 32 cephalometric points: 11 soft tissue landmarks and 21 hard tissue landmarks. The mean radical errors (MRE) were assessed radical to the threshold of 1.0mm,1.5mm, and 2 mm to compare the successful detection rate (SDR). One-way ANOVA analysis at significance level of P < .05 was used to compare MRE and SDR. The SPSS (IBM-vs. 27.0) and PRISM (GraphPad-vs.8.0.2) software were used for the data analysis. Results: Experimental results showed that three methods were able to achieve detection rates greater than 85% using the 2 mm precision threshold, which is the acceptable range in clinical practice. The Angelalign group even achieved a detection rate greater than 78.08% using the 1.0 mm threshold. A marked difference of time was found between the AI-assisted group and the manual group due to heterogeneity in the performance of techniques to detect the same landmark. Conclusions: AI assistance may increase efficiency without compromising accuracy with cephalometric tracings in routine clinical practice and in research settings.