A Deep Learning Approach to Submerged Teeth Classification and Detection

NCT04309851 | Completed

• 1 other identifier: E86412-49
• Study Type: observational
• Target: 74
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

artificial intelligence; infraocclusion; submerged tooth

Outcome Measures

Primary Outcomes (1)

• Submerged Tooth Detection

  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.

  6 months

Interventions

deep learning DIAGNOSTIC_TEST

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

Eligibility Criteria

• Age: 5 Years - 12 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17)
• Sampling Method: Probability Sample

Study Population

The data set included OPGs of 19000 children aged 5-12 years that were collected between January 2016 and December 2018.

You may not qualify if:

• OPG images of poor quality (metal artifact, artifacts due to position errors during shooting, etc.) were excluded.

Sponsors & Collaborators

• Eskisehir Osmangazi University: lead

Study Sites (1)

Seçil Çalışkan

Eskişehir, 26040, Turkey (Türkiye)

Location

MeSH Terms

Interventions

Deep Learning

Intervention Hierarchy (Ancestors)

Machine Learning; Artificial Intelligence; Algorithms; Mathematical Concepts; Neural Networks, Computer

Study Design

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

Study Record Dates

• First Submitted: March 12, 2020
• First Posted: March 16, 2020
• Study Start: January 1, 2019
• Primary Completion: January 1, 2020
• Study Completion: March 1, 2020
• Last Updated: March 16, 2020

Record last verified: 2020-03

Locations

TU (1)