Ignite Creation Date:
2025-12-24 @ 11:42 PM
Ignite Modification Date:
2025-12-31 @ 2:49 PM
Study NCT ID:
NCT04309851
Status:
COMPLETED
Last Update Posted:
2020-03-16
First Post:
2020-03-12
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
A Deep Learning Approach to Submerged Teeth Classification and Detection
Sponsor:
Eskisehir Osmangazi University
Organization:
Study Overview
Official Title:
A Deep Learning Approach to Submerged Deciduous Teeth Classification and Detection
Status:
COMPLETED
Status Verified Date:
2020-03
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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.
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.
Detailed Description:
Pre-processing, Training, and Classification The study was conducted with balanced data sets. The case and control data sets were randomly divided into two parts, the training group (27 case group/27 control group) and the test group (10 case group/10 control group) to prevent the use of the visuals in the training group for retesting. The testing data set was not seen by the system during the training phase.
All 2943-by-1435 pixel images in the data set were resized to 971 by 474 pixels prior to training. All OPG images used include the whole dentitions. The training and test data sets were used to estimate and generate weight factors for the optimal CNN algorithm. An arbitrary sequence was generated using open-source Python programming (Python 3.6.1, Python Software Foundation, Wilmington, DE, USA, https://www.python.org/) language and OpenCV, NumPy, Pandas, and Matplotlib libraries. In this study, Tensorflow for model development was used to classify submerged primary molars. InceptionV3 architecture was used as transfer learning, and the transfer values were saved in the cache. Then, fully connected layer and softmax classifiers were combined to form the final model layers. The training was carried out using 7000 steps with 16G RAM and a PC equipped with NVIDIA GeForce GTX 1050.
All 2943-by-1435 pixel images in the data set were resized to 971 by 474 pixels prior to training. All OPG images used include the whole dentitions. The training and test data sets were used to estimate and generate weight factors for the optimal CNN algorithm. An arbitrary sequence was generated using open-source Python programming (Python 3.6.1, Python Software Foundation, Wilmington, DE, USA, https://www.python.org/) language and OpenCV, NumPy, Pandas, and Matplotlib libraries. In this study, Tensorflow for model development was used to classify submerged primary molars. InceptionV3 architecture was used as transfer learning, and the transfer values were saved in the cache. Then, fully connected layer and softmax classifiers were combined to form the final model layers. The training was carried out using 7000 steps with 16G RAM and a PC equipped with NVIDIA GeForce GTX 1050.
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: