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Microbial Dental Plaque Analysis in Young Permanent Teeth Using Deep Learning

NCT06603233 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 31

Last updated 2024-09-19

No results posted yet for this study

Summary

Background: Dental plaque contributes to a number of common oral conditions such as caries, gingivitis, and periodontitis. As a result, detection and management of plaque is of great importance for the oral health of individuals. The primary objectives of this study were to design a deep learning model for the detection and segmentation of plaque in young permanent teeth and to evaluate the diagnostic accuracy of the model. Methods: The dataset contains 506 dental images from 31 patients aged 8 to 13 years. Six state-of-the-art models were trained and tested using this dataset. The U-Net Transformer model was compared with three dentists for clinical applicability using 35 randomly selected images from the test set.

Conditions

  • Dental Plaque (Diagnosis)
  • Deep Learning

Interventions

DIAGNOSTIC_TEST

The Difference Between The AI Model and Dentists Group

The clinical feasibility of the best performing model, statistical hypothesis tests are performed that compares the predictions of the AI model with the assessments from three dentists.

DIAGNOSTIC_TEST

Deep Learning Models

DeepLabV3+, Mask R-CNN (Detectron2), YOLOv8, U-Net, Super Vision U-net and U-Net Transformer were trained on 354 images and tested on 79 images.

Sponsors & Collaborators

  • Ankara Medipol University

    lead OTHER

Principal Investigators

  • Banu Çiçek Tez, Ph.D · Ankara Medipol University

Study Design

Allocation
NON_RANDOMIZED
Purpose
DIAGNOSTIC
Masking
TRIPLE
Model
FACTORIAL

Eligibility

Min Age
8 Years
Max Age
13 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-06-01
Primary Completion
2023-11-01
Completion
2023-11-01

Countries

  • Turkey (Türkiye)

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 NCT06603233 on ClinicalTrials.gov