This study aims to evaluate the accuracy and reliability of artificial intelligence (AI)-based cephalometric analysis compared with digital manual tracing. A total of 100 standardized lateral cephalometric radiographs will be analyzed using Delta-Dent software with manual landmark identification and three fully automated AI-based systems (WebCeph, QuantX, and Smartee). Sagittal, vertical, dental, and soft tissue cephalometric parameters will be compared among the different methods. Statistical analysis will assess inter-method agreement and the clinical relevance of any observed discrepancies. The study seeks to determine whether AI-based systems provide measurements comparable to conventional digital tracing and whether they can be considered reliable adjunctive tools in orthodontic diagnosis and treatment planning.

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.

Inclusion Criteria:

• Availability of digital lateral cephalometric radiographs of adequate diagnostic quality
• Radiographs acquired with patients in centric occlusion and proper head positioning using a cephalostat
• Patients of any age and sex
• Absence of congenital or acquired craniofacial anomalies
• No previous orthodontic treatment
• No previous orthognathic surgical treatment
• Absence of agenesis of incisors or first molars
• Absence of supernumerary teeth overlapping the region of interest

Exclusion Criteria:

• Radiographs presenting artifacts or inadequate visualization of anatomical structures
• History of significant craniofacial trauma
• Radiographs acquired without a cephalostat
• Presence of severe skeletal asymmetries
• Incomplete clinical or radiographic records
• Radiographs unsuitable for manual or AI-based cephalometric landmark identification

The study population consists of patients of any age and sex who underwent digital lateral cephalometric radiography as part of routine orthodontic diagnostic records at the Unit of Orthodontics and Paediatric Dentistry, University of Pavia. Radiographs meeting the predefined inclusion and exclusion criteria will be retrospectively selected for analysis.

Data will be available upon motivated request to the corresponding authors.