Artificial Intelligence-Based Assessment of Endosseous Lesions (AIpreop)
Artificial Intelligence-Based Assessment of Endosseous Lesions: A Prospective Clinical Study
Sponsor: University of Bari Aldo Moro
A observational or N/A phase clinical study on Mandibular Cyst and Maxillary Cyst, this trial is actively recruiting participants. The trial is conducted by University of Bari Aldo Moro and has accumulated 1 data snapshot since 2026. Longitudinal tracking of this trial contributes to a broader understanding of treatment development timelines.
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1 version recordedEligibility Summary
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.
Contact Information
- University of Bari Aldo Moro
For direct contact, visit the study record on ClinicalTrials.gov .