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
University of Pavia
Lead sponsor