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.
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
Radiographs presenting artifacts or inadequate visualization of anatomical structures
History of significant craniofacial trauma
Radiographs acquired without a cephalostat
Presence of severe skeletal asymmetries
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.
Cohort
Prospective
Cephalometric Analysis
Diagnostic testAll included lateral cephalometric radiographs will undergo cephalometric analysis using both digital manual tracing and artificial intelligence-based automated systems. Manual digital tracing will be performed with Delta-Dent software by a single experienced orthodontist through manual identification of cephalometric landmarks. The same radiographs will subsequently be analysed using three fully automated AI-based software programs (WebCeph™, QuantX, and Smartee) without manual correction of landmark positioning. No therapeutic intervention or modification of patient treatment will be performed, as this is an observational comparative study based exclusively on retrospective analysis of radiographic records.
Agreement between AI-based cephalometric analysis and digital manual tracing
Agreement between cephalometric measurements obtained with AI-based software systems and digital manual tracing will be assessed using the intraclass correlation coefficient (ICC) and differences in angular and linear cephalometric measurements.
SNA
SNB
ANB
SN-GoGn
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