Expert System for Mandibular Condyle Detection and Osteoarthritis Classification in Panoramic Imaging Using R-CNN and CNN
Temporomandibular joint osteoarthritis (TMJ OA) is a degenerative condition of the TMJ led by a pathological tissue response of the joint under mechanical loading. It is characterized by the progressive destruction of the internal surfaces of the joint, which can result in debilitating pain and join...
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doaj-68c544f46b8f41519d697dcac645f14d2020-11-25T03:56:00ZengMDPI AGApplied Sciences2076-34172020-10-01107464746410.3390/app10217464Expert System for Mandibular Condyle Detection and Osteoarthritis Classification in Panoramic Imaging Using R-CNN and CNNDonghyun Kim0Eunhye Choi1Ho Gul Jeong2Joonho Chang3Sekyoung Youm4Department of Advanced General Dentistry, Yonsei University College of Dentistry, Seoul 03722, KoreaDepartment of Oral Medicine & Oral Diagnosis, School of Dentistry & Dental Research Institute, Seoul National University, Seoul 08826, KoreaAIQUB Co., Ltd, Seoul 05854, KoreaDepartment of Industrial and Systems Engineering, Dongguk University, Seoul 04620, KoreaDepartment of Industrial and Systems Engineering, Dongguk University, Seoul 04620, KoreaTemporomandibular joint osteoarthritis (TMJ OA) is a degenerative condition of the TMJ led by a pathological tissue response of the joint under mechanical loading. It is characterized by the progressive destruction of the internal surfaces of the joint, which can result in debilitating pain and joint noise. Panoramic imaging can be used as a basic screening tool with thorough clinical examination in diagnosing TMJ OA. This paper proposes an algorithm that can extract the condylar region and determine its abnormality by using convolutional neural networks (CNNs) and Faster region-based CNNs (R-CNNs). Panoramic images are collected retrospectively and 1000 images are classified into three categories—normal, abnormal, and unreadable—by a dentist or orofacial pain specialist. Labels indicating whether the condyle is detected and its location enabled more clearly recognizable panoramic images. The uneven proportion of normal to abnormal data is adjusted by duplicating and rotating the images. An R-CNN model and a Visual Geometry Group-16 (VGG16) model are used for learning and condyle discrimination, respectively. To prevent overfitting, the images are rotated ±10° and shifted by 10%. The average precision of condyle detection using an R-CNN at intersection over union (IoU) >0.5 is 99.4% (right side) and 100% (left side). The sensitivity, specificity, and accuracy of the TMJ OA classification algorithm using a CNN are 0.54, 0.94, and 0.84, respectively. The findings demonstrate that classifying panoramic images through CNNs is possible. It is expected that artificial intelligence will be more actively applied to analyze panoramic X-ray images in the future.https://www.mdpi.com/2076-3417/10/21/7464medical information expert systemsneural networksosteoarthritispanoramic radiographytemporomandibular joint |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Donghyun Kim Eunhye Choi Ho Gul Jeong Joonho Chang Sekyoung Youm |
spellingShingle |
Donghyun Kim Eunhye Choi Ho Gul Jeong Joonho Chang Sekyoung Youm Expert System for Mandibular Condyle Detection and Osteoarthritis Classification in Panoramic Imaging Using R-CNN and CNN Applied Sciences medical information expert systems neural networks osteoarthritis panoramic radiography temporomandibular joint |
author_facet |
Donghyun Kim Eunhye Choi Ho Gul Jeong Joonho Chang Sekyoung Youm |
author_sort |
Donghyun Kim |
title |
Expert System for Mandibular Condyle Detection and Osteoarthritis Classification in Panoramic Imaging Using R-CNN and CNN |
title_short |
Expert System for Mandibular Condyle Detection and Osteoarthritis Classification in Panoramic Imaging Using R-CNN and CNN |
title_full |
Expert System for Mandibular Condyle Detection and Osteoarthritis Classification in Panoramic Imaging Using R-CNN and CNN |
title_fullStr |
Expert System for Mandibular Condyle Detection and Osteoarthritis Classification in Panoramic Imaging Using R-CNN and CNN |
title_full_unstemmed |
Expert System for Mandibular Condyle Detection and Osteoarthritis Classification in Panoramic Imaging Using R-CNN and CNN |
title_sort |
expert system for mandibular condyle detection and osteoarthritis classification in panoramic imaging using r-cnn and cnn |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2020-10-01 |
description |
Temporomandibular joint osteoarthritis (TMJ OA) is a degenerative condition of the TMJ led by a pathological tissue response of the joint under mechanical loading. It is characterized by the progressive destruction of the internal surfaces of the joint, which can result in debilitating pain and joint noise. Panoramic imaging can be used as a basic screening tool with thorough clinical examination in diagnosing TMJ OA. This paper proposes an algorithm that can extract the condylar region and determine its abnormality by using convolutional neural networks (CNNs) and Faster region-based CNNs (R-CNNs). Panoramic images are collected retrospectively and 1000 images are classified into three categories—normal, abnormal, and unreadable—by a dentist or orofacial pain specialist. Labels indicating whether the condyle is detected and its location enabled more clearly recognizable panoramic images. The uneven proportion of normal to abnormal data is adjusted by duplicating and rotating the images. An R-CNN model and a Visual Geometry Group-16 (VGG16) model are used for learning and condyle discrimination, respectively. To prevent overfitting, the images are rotated ±10° and shifted by 10%. The average precision of condyle detection using an R-CNN at intersection over union (IoU) >0.5 is 99.4% (right side) and 100% (left side). The sensitivity, specificity, and accuracy of the TMJ OA classification algorithm using a CNN are 0.54, 0.94, and 0.84, respectively. The findings demonstrate that classifying panoramic images through CNNs is possible. It is expected that artificial intelligence will be more actively applied to analyze panoramic X-ray images in the future. |
topic |
medical information expert systems neural networks osteoarthritis panoramic radiography temporomandibular joint |
url |
https://www.mdpi.com/2076-3417/10/21/7464 |
work_keys_str_mv |
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