Artificial Intelligence-Based Assessment of Endosseous Lesions

NCT07505485 | Recruiting DMC

• 1 other identifier: AIpreoperative
• Study Type: interventional
• Target: 10
• Locations: 1 country
• Sites: 2

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.

Conditions

Maxillary Cyst; Mandibular Cyst

Outcome Measures

Primary Outcomes (1)

• Time required for CBCT interpretation (minutes)

  assessment of the time required for CBCT interpretation by the surgeon. A digital stopwatch was used to record the operative time required for each procedural step, with measurements expressed in seconds, in order to obtain an objective and standardized assessment of execution time.

  Day 1

Secondary Outcomes (1)

• Intraoperative and Postoperative Complications

  Day 1

Study Arms (2)

Patients with endosseous lesion-Analyzed using conventional CBCT

NO INTERVENTION

Patients with endosseous lesion- AI-assisted evaluation

EXPERIMENTAL

* Automated segmentation of the lesion * 3D reconstruction * Volumetric calculation

Diagnostic Test: AI assisted Evaluation

Interventions

AI assisted Evaluation DIAGNOSTIC_TEST

CBCT scans were processed using AI-based software capable of: * Automated segmentation of the lesion * 3D reconstruction * Volumetric calculation

Patients with endosseous lesion- AI-assisted evaluation

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

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

You may not qualify if:

• Smoking more than 15 cigarettes a day
• Pregnancy
• Acute infections

Sponsors & Collaborators

• University of Bari Aldo Moro: lead

Study Sites (2)

University of Bari Aldo Moro

Bari, 70021, Italy

RECRUITING

Dr. Giuseppe D'Albis

Bari, 70124, Italy

RECRUITING

Study Officials

• Giuseppe D'Albis, Dr

  University of Bari Aldo Moro

  PRINCIPAL INVESTIGATOR

• Saverio Capodiferro, Prof

  University of Bari Aldo Moro

  STUDY DIRECTOR

Central Study Contacts

Giuseppe D'Albis, Dr.

CONTACT

+393495103642; giuseppe.dalbis@uniba.it

Saverio Capodiferro, Prof.

CONTACT

saverio.capodiferro@uniba.it

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: March 21, 2026
• First Posted: April 1, 2026
• Study Start: April 1, 2026
• Primary Completion: May 1, 2026
• Study Completion: May 1, 2026
• Last Updated: April 15, 2026

Record last verified: 2026-04

Locations

IT (2)