Comparison of Digital Analysis and Artificial Intelligence for Cephalometric Tracing

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.

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