Artificial Intelligence-Based Assessment of Endosseous Lesions

ClinicalTrials.gov ID

NCT07505485

Lead Sponsor

University of Bari Aldo MoroOTHER

Overall Status

Recruiting

Study Type

Interventional

Phase

Not Applicable

Enrollment

10Estimated

Last Update Posted

Apr 15, 2026Actual

Start Date

Apr 1, 2026Estimated

Primary Completion Date

May 1, 2026Estimated

Completion Date

May 1, 2026Estimated

Official Title

Artificial Intelligence-Based Assessment of Endosseous Lesions: A Prospective Clinical Study

Brief 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.

• Maxillary Cyst
• Mandibular Cyst

• AI assisted Evaluation

Ages Eligible for Study

Adult (18-64)Older Adult (65+)

Sexes Eligible for Study

All

Accepts Healthy Volunteers

Yes

Eligibility Criteria

Inclusion Criteria:

• Good health according to the System of the American Society of Anesthesiology
• Aged older than 18 years
• No general medical contraindication for surgery

Exclusion Criteria:

• Smoking more than 15 cigarettes a day

  • Pregnancy
  • Acute infections

Primary Purpose

Diagnostic

Allocation

Randomized

Interventional Model

Parallel Assignment

Masking

None (Open Label)

Lead Sponsor

University of Bari Aldo MoroOTHER

giuseppe.dalbis@uniba.it+393495103642

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