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NCT Number: NCT07505485

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.

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Key information

Conditions

Maxillary Cyst Mandibular Cyst

Age range

18 year–80 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Dr. Giuseppe D'Albis, Bari, Italy

Loading trial locations.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

AI assisted Evaluation

Diagnostic Test

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

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

Primary outcomes

  1. 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.

Secondary outcomes

  1. 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

Study contacts

Contact information is provided by the study sponsor or research team.

Giuseppe D'Albis, Dr.

CONTACT

[email protected]

+393495103642

Saverio Capodiferro, Prof.

CONTACT

[email protected]

Sponsors and collaborators

Lead sponsor

University of Bari Aldo Moro

Other

Registry information

Official study title

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

Acronym: AIpreop

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Apr 1, 2026
Registry last updated
Apr 15, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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