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Transfer Learning via Deep Neural Networks for Implant Fixture System Classification Using Periapical Radiographs
- Source :
- Journal of Clinical Medicine, Volume 9, Issue 4, Journal of Clinical Medicine, Vol 9, Iss 1117, p 1117 (2020)
- Publication Year :
- 2020
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2020.
-
Abstract
- In the absence of accurate medical records, it is critical to correctly classify implant fixture systems using periapical radiographs to provide accurate diagnoses and treatments to patients or to respond to complications. The purpose of this study was to evaluate whether deep neural networks can identify four different types of implants on intraoral radiographs. In this study, images of 801 patients who underwent periapical radiographs between 2005 and 2019 at Yonsei University Dental Hospital were used. Images containing the following four types of implants were selected: Br&aring<br />nemark Mk TiUnite, Dentium Implantium, Straumann Bone Level, and Straumann Tissue Level. SqueezeNet, GoogLeNet, ResNet-18, MobileNet-v2, and ResNet-50 were tested to determine the optimal pre-trained network architecture. The accuracy, precision, recall, and F1 score were calculated for each network using a confusion matrix. All five models showed a test accuracy exceeding 90%. SqueezeNet and MobileNet-v2, which are small networks with less than four million parameters, showed an accuracy of approximately 96% and 97%, respectively. The results of this study confirmed that convolutional neural networks can classify the four implant fixtures with high accuracy even with a relatively small network and a small number of images. This may solve the inconveniences associated with unnecessary treatments and medical expenses caused by lack of knowledge about the exact type of implant.
- Subjects :
- Radiography
lcsh:Medicine
Implant fixture classification
Fixture
Convolutional neural network
Article
03 medical and health sciences
0302 clinical medicine
periapical radiographs
convolutional neural networks
Medicine
Medical diagnosis
030304 developmental biology
Orthodontics
0303 health sciences
business.industry
Deep learning
lcsh:R
Confusion matrix
deep learning
030206 dentistry
General Medicine
artificial intelligence
Artificial intelligence
Implant
business
F1 score
Subjects
Details
- Language :
- English
- ISSN :
- 20770383
- Database :
- OpenAIRE
- Journal :
- Journal of Clinical Medicine
- Accession number :
- edsair.doi.dedup.....ca52b96092c453d471e4ef45467320bc
- Full Text :
- https://doi.org/10.3390/jcm9041117