Artificial Intelligence-Based Assessment of Endosseous Lesions (AIpreop)

April 12, 2026 updated by: Giuseppe D'Albis, University of Bari Aldo Moro

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

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Interventional

Enrollment (Estimated)

10

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • Italy
      • Bari, Italy, 70021
      • Bari, Italy, 70124
        • Recruiting
        • Dr. Giuseppe D'Albis
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
No Intervention: Patients with endosseous lesion-Analyzed using conventional CBCT
Experimental: Patients with endosseous lesion- AI-assisted evaluation
  • Automated segmentation of the lesion
  • 3D reconstruction
  • Volumetric calculation
Diagnostic test: AI assisted Evaluation

CBCT scans were processed using AI-based software capable of:

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Time required for CBCT interpretation (minutes)
Time Frame: Day 1
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 Outcome Measures

Outcome Measure
Measure Description
Time Frame
Intraoperative and Postoperative Complications
Time Frame: Day 1
  • Unexpected endodontic treatment of adjacent teeth
  • Intraoperative nerve exposure
  • Paresthesia
  • Excessive bone removal
  • Incomplete lesion removal
  • Postoperative infection
  • Delayed healing
  • Sinus involvement
  • Root damage to adjacent teeth
Day 1

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

University of Bari Aldo Moro

Investigators

  • Principal Investigator: Giuseppe D'Albis, Dr, University of Bari Aldo Moro
  • Study Director: Saverio Capodiferro, Prof, University of Bari Aldo Moro

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

April 1, 2026

Primary Completion (Estimated)

May 1, 2026

Study Completion (Estimated)

May 1, 2026

Study Registration Dates

First Submitted

March 21, 2026

First Submitted That Met QC Criteria

March 29, 2026

First Posted (Actual)

April 1, 2026

Study Record Updates

Last Update Posted (Actual)

April 15, 2026

Last Update Submitted That Met QC Criteria

April 12, 2026

Last Verified

April 1, 2026

More Information

Terms related to this study

Other Study ID Numbers

  • AIpreoperative

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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