Chisako Muramatsu1, Takumi Morishita2, Ryo Takahashi3, Tatsuro Hayashi3, Wataru Nishiyama4, Yoshiko Ariji5, Xiangrong Zhou6, Takeshi Hara6, Akitoshi Katsumata4, Eiichiro Ariji5, Hiroshi Fujita6. 1. Faculty of Data Science, Shiga University, 1-1-1 Banba, Hikone, Shiga, 522-8222, Japan. chisako-muramatsu@biwako.shiga-u.ac.jp. 2. Department of Intelligence Science and Engineering, Graduate School of Natural Science and Technology, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan. 3. Media Co. Ltd, 3-26-6 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan. 4. Department of Oral Radiology, Asahi University School of Dentistry, 1851 Hozumi, Mizuho, Gifu, 501-0296, Japan. 5. Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 1-100 Kusumotocho, Chikusa-ku, Nagoya, Aichi, 464-8650, Japan. 6. Department of Electrical, Electronic and Computer Engineering, Gifu University, 1-1 Yanagido, Gifu, 501-1194, Japan.
Abstract
OBJECTIVES: Dental state plays an important role in forensic radiology in case of large scale disasters. However, dental information stored in dental clinics are not standardized or electronically filed in general. The purpose of this study is to develop a computerized system to detect and classify teeth in dental panoramic radiographs for automatic structured filing of the dental charts. It can also be used as a preprocessing step for computerized image analysis of dental diseases. METHODS: One hundred dental panoramic radiographs were employed for training and testing an object detection network using fourfold cross-validation method. The detected bounding boxes were then classified into four tooth types, including incisors, canines, premolars, and molars, and three tooth conditions, including nonmetal restored, partially restored, and completely restored, using classification network. Based on the visualization result, multisized image data were used for the double input layers of a convolutional neural network. The result was evaluated by the detection sensitivity, the number of false-positive detection, and classification accuracies. RESULTS: The tooth detection sensitivity was 96.4% with 0.5 false positives per case. The classification accuracies for tooth types and tooth conditions were 93.2% and 98.0%. Using the double input layer network, 6 point increase in classification accuracy was achieved for the tooth types. CONCLUSIONS: The proposed method can be useful in automatic filing of dental charts for forensic identification and preprocessing of dental disease prescreening purposes.
OBJECTIVES: Dental state plays an important role in forensic radiology in case of large scale disasters. However, dental information stored in dental clinics are not standardized or electronically filed in general. The purpose of this study is to develop a computerized system to detect and classify teeth in dental panoramic radiographs for automatic structured filing of the dental charts. It can also be used as a preprocessing step for computerized image analysis of dental diseases. METHODS: One hundred dental panoramic radiographs were employed for training and testing an object detection network using fourfold cross-validation method. The detected bounding boxes were then classified into four tooth types, including incisors, canines, premolars, and molars, and three tooth conditions, including nonmetal restored, partially restored, and completely restored, using classification network. Based on the visualization result, multisized image data were used for the double input layers of a convolutional neural network. The result was evaluated by the detection sensitivity, the number of false-positive detection, and classification accuracies. RESULTS: The tooth detection sensitivity was 96.4% with 0.5 false positives per case. The classification accuracies for tooth types and tooth conditions were 93.2% and 98.0%. Using the double input layer network, 6 point increase in classification accuracy was achieved for the tooth types. CONCLUSIONS: The proposed method can be useful in automatic filing of dental charts for forensic identification and preprocessing of dental disease prescreening purposes.
Authors: Abdullah S Al-Malaise Al-Ghamdi; Mahmoud Ragab; Saad Abdulla AlGhamdi; Amer H Asseri; Romany F Mansour; Deepika Koundal Journal: Comput Intell Neurosci Date: 2022-04-30
Authors: Nektarios Tsoromokos; Sarah Parinussa; Frank Claessen; David Anssari Moin; Bruno G Loos Journal: Int Dent J Date: 2022-05-13 Impact factor: 2.607