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Transfer Learning via Deep Neural Networks for Implant Fixture System Classification Using Periapical Radiographs

 Jong-Eun Kim  ;  Na-Eun Nam  ;  June-Sung Shim  ;  Yun-Hoa Jung  ;  Bong-Hae Cho  ;  Jae Joon Hwang 
 JOURNAL OF CLINICAL MEDICINE, Vol.9(4) : 1117, 2020-04 
Journal Title
Issue Date
Implant fixture classification ; artificial intelligence ; convolutional neural networks ; deep learning ; periapical radiographs
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: Brånemark 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.
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2. College of Dentistry (치과대학) > Dept. of Prosthodontics (보철과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Jong Eun(김종은) ORCID logo https://orcid.org/0000-0002-7834-2524
Shim, June Sung(심준성) ORCID logo https://orcid.org/0000-0003-1428-0122
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