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AI-Based Radiographic Detection of Periodontal Defects

NCT07086625 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 500

Last updated 2025-07-25

No results posted yet for this study

Summary

The primary objective of the study is to develop and validate a machine learning model for the automatic identification of periodontal vertical bone defects, improving diagnostic accuracy and efficiency.

The study comprises three phases:

1. Public dataset annotation: Approximately 7,000 intraoral radiographs will be manually annotated by experts to classify periodontal bone defects (1-wall, 2+ walls, craters, furcation involvement).
2. Model training: A deep learning algorithm will be trained on the annotated images to learn automatic recognition of the defects.
3. Clinical validation: The model will be tested on a dataset of 150 anonymized radiographs from 20-30 patients treated at AOU (Azienda Ospedaliero Universitaria) Cagliari, comparing its performance to expert dental evaluations.

Conditions

  • Periodontitis

Sponsors & Collaborators

  • University of L'Aquila

    collaborator OTHER

  • University of Cagliari

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-01-01
Primary Completion
2024-12-31
Completion
2025-03-31

Countries

  • Italy

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07086625 on ClinicalTrials.gov