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

Jong-Eun Kim,Jae Joon Hwang,Bong-Hae Cho,Yun-Hoa Jung,Na-Eun Nam,June-Sung Shim

doi:10.3390/jcm9041117

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: 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.

Highlights

Since Professor Brånemark’s introduction of the concept of osseointegration in the 1960s through preclinical and clinical studies, implant dentistry has developed rapidly, becoming a common treatment for tooth loss [1,2,3]
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Summary

Introduction

Since Professor Brånemark’s introduction of the concept of osseointegration in the 1960s through preclinical and clinical studies, implant dentistry has developed rapidly, becoming a common treatment for tooth loss [1,2,3]. Dental implants show much better long-term stability compared to conventional fixed partial dentures or removable dental prostheses, with many studies reporting survival rates of more than 95% for dental implants [11,12]. Continued developments in this area have led to the availability of a variety of implant systems in the market in recent years [13,14,15]. The number of implant brands in the market has increased since the publication of this study

Objectives

Methods

Results

Discussion

Conclusion