AI-Based Radiographic Detection of Periodontal Defects

NCT07086625 | Completed DMC

• 1 other identifier: PerioDefects-AI
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 1

Brief 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. 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. 2.Model training: A deep learning algorithm will be trained on the annotated images to learn automatic recognition of the defects.
3. 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

Keywords

Periodontal infrabony defects

Outcome Measures

Primary Outcomes (3)

• Intersection over Union (IoU)

  The IoU measures the overlap between a predicted bounding box and a ground truth bounding box. It is defined as: Area of Overlap/Area of Union; where the area of overlap is the intersection of the predicted and ground truth boxes, and the area of union is the total area covered by both boxes.

  Baseline

• Precision (P)

  The fraction of true positives (TP) among all predictions: T P/T P + F P High precision indicates that the model makes few false positive (FP) predictions.

  Baseline

• Recall (R)

  The fraction of true positives among all ground truth objects: T P/T P + F N (false negatives) High recall indicates that the model detects most ground truth objects.

  Baseline

Study Arms (1)

intraoral radiographs

Intraoral radiographs images showing periodontal infrabony defects

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

A dataset of intraoral radiographs

You may qualify if:

• Intraoral radiographs showing presence of periodontal infrabony defects

You may not qualify if:

• Intraoral radiographs showing without detectable presence of periodontal infrabony defects

Sponsors & Collaborators

• University of Cagliari: lead
• University of L'Aquila: collaborator

Study Sites (1)

Università degli Studi di Cagliari

Cagliari, California, 09042, Italy

Location

MeSH Terms

Conditions

Periodontitis

Condition Hierarchy (Ancestors)

Periodontal Diseases; Mouth Diseases; Stomatognathic Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor

Study Record Dates

• First Submitted: July 11, 2025
• First Posted: July 25, 2025
• Study Start: January 1, 2024
• Primary Completion: December 31, 2024
• Study Completion: March 31, 2025
• Last Updated: July 25, 2025

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)