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A Deep Learning Approach to Submerged Teeth Classification and Detection

NCT04309851 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 74

Last updated 2020-03-16

No results posted yet for this study

Summary

Objectives: The study aimed to compare the success and reliability of an artificial intelligence application in the detection and classification of submerged teeth in orthopantomography (OPG).

Methods: Convolutional neural networks (CNN) algorithms were used to detect and classify submerged molars. The detection module, which is based on the state-of-the-art Faster R-CNN architecture, processed the radiograph to define the boundaries of submerged molars. A separate testing set was used to evaluate the diagnostic performance of the system and compare it to the expert level.

Results: The success rate of classification and identification of the system is high when evaluated according to the reference standard. The system was extremely accurate in performance comparison with observers.

Conclusions: The performance of the proposed computer-aided diagnosis solution is comparable to that of experts. It is useful to diagnose submerged molars with an artificial intelligence application to prevent errors. Also, it will facilitate pediatric dentists' diagnoses.

Conditions

  • Artificial Intelligence
  • Submerging Tooth

Interventions

DIAGNOSTIC_TEST

deep learning

the deep learning method is a field of study involving artificial neural networks and similar machine learning algorithms with many hidden layers.

Sponsors & Collaborators

  • Eskisehir Osmangazi University

    lead OTHER

Eligibility

Min Age
5 Years
Max Age
12 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2019-01-01
Primary Completion
2020-01-01
Completion
2020-03-01

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 NCT04309851 on ClinicalTrials.gov