Comparison of Digital Analysis and Artificial Intelligence for Cephalometric Tracing

This prospective observational study aims to evaluate the accuracy, reproducibility, and clinical reliability of artificial intelligence (AI)-based cephalometric analysis systems compared with digital manual tracing. Patients whose lateral cephalometric radiographs were previously acquired for orthodontic diagnostic purposes at the Unit of Orthodontics and Paediatric Dentistry, University of Pavia, will be retrospectively selected according to predefined inclusion and exclusion criteria. Written informed consent for the use of clinical records for research purposes will be obtained from all participants or their legal guardians. A total of 100 standardized digital lateral cephalometric radiographs will be included in the study. Each radiograph will be analysed using Delta-Dent software with manual landmark identification and three fully automated AI-based software systems: WebCeph™, QuantX, and Smartee. Cephalometric analyses will be performed without manual adjustment of landmarks in the AI-based systems. Ten cephalometric parameters representative of sagittal, vertical, dental, and soft tissue relationships will be evaluated, including SNA, SNB, ANB, SN-GoGn, L1-GoGn, U1-ANSPNS, nasolabial angle, facial angle, Wits appraisal, and N-Me. Manual digital tracing performed by a single experienced orthodontist will be considered the reference method. Intra-rater reliability will be assessed using intraclass correlation coefficient (ICC). Statistical analysis will be conducted using R software (version 3.1.3; R Foundation for Statistical Computing, Wien, Austria). Descriptive statistics will be calculated for all variables. Normality of data distribution will be assessed using the Kolmogorov-Smirnov test. Comparisons among the different methods will be performed using the Friedman test followed by Dunn's post hoc test. Statistical significance will be predetermined at p < 0.05.