Deep Convolutional Neural Networks Based Analysis of Cephalometric Radiographs for Differential Diagnosis of Orthognathic Surgery Indications

Lee, Ki-Sun; Ryu, Jae-Jun; Jang, Hyon Seok; Lee, Dong-Yul; Jung, Seok-Ki

doi:10.3390/app10062124

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Deep Convolutional Neural Networks Based Analysis of Cephalometric Radiographs for Differential Diagnosis of Orthognathic Surgery Indications

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Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul 03080, Korea

Department of Clinical Dentistry, College of Medicine, Korea University, Seoul 02841, Korea

Department of Prosthodontics, Korea University Ansan Hospital, Gyung-gi do 15355, Korea

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Department of Prosthodontics, Korea University Anam Hospital, Seoul 02841, Korea

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Department of Oral and Maxillofacial Surgery, Korea University Ansan Hospital, Gyung-gi do 15355, Korea

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Department of Orthodontics, Korea University Guro Hospital, Seoul 08308, Korea

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Department of Orthodontics, Korea University Ansan Hospital, Gyung-gi do 15355, Korea

Authors to whom correspondence should be addressed.

Appl. Sci. 2020, 10(6), 2124; https://doi.org/10.3390/app10062124

Received: 13 February 2020 / Revised: 9 March 2020 / Accepted: 11 March 2020 / Published: 20 March 2020

(This article belongs to the Section Applied Dentistry and Oral Sciences)

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Abstract

The aim of this study was to evaluate the deep convolutional neural networks (DCNNs) based on analysis of cephalometric radiographs for the differential diagnosis of the indications of orthognathic surgery. Among the DCNNs, Modified-Alexnet, MobileNet, and Resnet50 were used, and the accuracy of the models was evaluated by performing 4-fold cross validation. Additionally, gradient-weighted class activation mapping (Grad-CAM) was used to perform visualized interpretation to determine which region affected the DCNNs’ class classification. The prediction accuracy of the models was 96.4% for Modified-Alexnet, 95.4% for MobileNet, and 95.6% for Resnet50. According to the Grad-CAM analysis, the most influential regions for the DCNNs’ class classification were the maxillary and mandibular teeth, mandible, and mandibular symphysis. This study suggests that DCNNs-based analysis of cephalometric radiograph images can be successfully applied for differential diagnosis of the indications of orthognathic surgery. View Full-Text

Keywords: artificial intelligence; convolutional neural networks; cephalometric radiographs; orthognathic surgery

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MDPI and ACS Style

Lee, K.-S.; Ryu, J.-J.; Jang, H.S.; Lee, D.-Y.; Jung, S.-K. Deep Convolutional Neural Networks Based Analysis of Cephalometric Radiographs for Differential Diagnosis of Orthognathic Surgery Indications. Appl. Sci. 2020, 10, 2124. https://doi.org/10.3390/app10062124

AMA Style

Lee K-S, Ryu J-J, Jang HS, Lee D-Y, Jung S-K. Deep Convolutional Neural Networks Based Analysis of Cephalometric Radiographs for Differential Diagnosis of Orthognathic Surgery Indications. Applied Sciences. 2020; 10(6):2124. https://doi.org/10.3390/app10062124

Chicago/Turabian Style

Lee, Ki-Sun, Jae-Jun Ryu, Hyon S. Jang, Dong-Yul Lee, and Seok-Ki Jung. 2020. "Deep Convolutional Neural Networks Based Analysis of Cephalometric Radiographs for Differential Diagnosis of Orthognathic Surgery Indications" Applied Sciences 10, no. 6: 2124. https://doi.org/10.3390/app10062124

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Deep Convolutional Neural Networks Based Analysis of Cephalometric Radiographs for Differential Diagnosis of Orthognathic Surgery Indications

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