Artificial Intelligence-Based Assessment of Endosseous Lesions

Despite these advances, CBCT interpretation remains largely qualitative and dependent on the clinician's experience. Conventional evaluation is based on two-dimensional slices and linear measurements, which may underestimate lesion complexity and spatial distribution. Recent developments in Artificial Intelligence in Medicine have introduced automated image segmentation tools capable of identifying lesion boundaries and calculating volumetric data. These technologies allow a transition from subjective assessment to objective, reproducible quantification. The potential clinical advantages include: * Objective measurement of lesion size (volume in mm³) * Improved surgical planning * Enhanced prediction of anatomical involvement * Reduction of diagnostic errors * Standardization of follow-up and outcome assessment Therefore, the aim of the present study was to evaluate the clinical impact of AI-based segmentation and volumetric analysis of endosseous lesions compared to conventional CBCT interpretation.

NCT Number	NCT07505485
Lead Sponsor	University of Bari Aldo Moro
Conditions	Maxillary Cyst, Mandibular Cyst
Enrollment	10 participants
Start Date	2026-04-01
Primary Completion	2026-05-01 (estimated)
Study Completion	2026-05-01 (estimated)
Updated on ClinicalTrials.gov	2026-04-15