Cephalometric tracing: comparing artificial intelligence and augmented intelligence on online platforms

This research aimed to evaluate the results of cephalometric analyses obtained by artificial intelligence (AI) from the RadioCef, EasyCeph, and WebCeph platforms, and their variability due to modifications made by the user.

In this cross-sectional observational study, seventy cephalometric radiographs were analysed using the AI of the platforms. Subsequently, four examiners with different areas of expertise and levels of experience examined each landmark, correcting its location if necessary.

The Pog, L1 tip, B, and Go landmarks on the RadioCef; Pn, Me, Pog, U1 tip, and UL on the EasyCeph; and Pog, Me, and B on the WebCeph showed a modification equal to or greater than 90%. More experienced examiners modified a greater number of landmarks. The repeated measures ANOVA test reported statistically significant differences concerning the SNA, SNB, ANB, SN-GoGn, FMIA, FMA, and IMPA angles (P < .05) for fully automated and semi-automated analyses. ICC values reported intra-observer agreement levels from poor (ICC = 0.27) to perfect (ICC = 1), and inter-observer agreement showed good to excellent reliability (ICC = 0.88-0.99).

Fully automated cephalometric analysis presents variations according to modifications made by the examiners. This represents a challenge to the knowledge of the orthodontist, influencing the diagnosis and treatment planning. Therefore, the use of augmented intelligence in cephalometric analysis is still suggested based on the results obtained for each platform.

Cephalometric AI platforms show variability in landmark location accuracy. User modifications significantly impact automated analysis results.