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Tytuł pozycji:

Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography.

Tytuł :
Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography.
Autorzy :
Murata M; Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
Ariji Y; Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan. .
Ohashi Y; Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
Kawai T; Department of Oral and Maxillofacial Radiology, School of Life Dentistry at Tokyo, Nippon Dental University, Tokyo, Japan.
Fukuda M; Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
Funakoshi T; Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
Kise Y; Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
Nozawa M; Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
Katsumata A; Department of Oral Radiology, Asahi University School of Dentistry, Mizuho, Japan.
Fujita H; Department of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu, Japan.
Ariji E; Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
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Źródło :
Oral radiology [Oral Radiol] 2019 Sep; Vol. 35 (3), pp. 301-307. Date of Electronic Publication: 2018 Dec 11.
Typ publikacji :
Journal Article
Język :
English
Imprint Name(s) :
Publication: 2004- : Tokyo : Springer-Verlag Tokyo
Original Publication: Gifu, Japan : Japanese Society of Dental Radiology,
MeSH Terms :
Deep Learning*
Maxillary Sinusitis*/diagnostic imaging
Neural Networks, Computer*
Radiography, Panoramic*
Area Under Curve ; Humans
References :
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Contributed Indexing :
Keywords: Artificial intelligence*; Computed tomography*; Deep learning*; Maxillary sinusitis*; Panoramic radiography*
Entry Date(s) :
Date Created: 20181213 Date Completed: 20200807 Latest Revision: 20200807
Update Code :
20210210
DOI :
10.1007/s11282-018-0363-7
PMID :
30539342
Czasopismo naukowe
Objectives: To apply a deep-learning system for diagnosis of maxillary sinusitis on panoramic radiography, and to clarify its diagnostic performance.
Methods: Training data for 400 healthy and 400 inflamed maxillary sinuses were enhanced to 6000 samples in each category by data augmentation. Image patches were input into a deep-learning system, the learning process was repeated for 200 epochs, and a learning model was created. Newly-prepared testing image patches from 60 healthy and 60 inflamed sinuses were input into the learning model, and the diagnostic performance was calculated. Receiver-operating characteristic (ROC) curves were drawn, and the area under the curve (AUC) values were obtained. The results were compared with those of two experienced radiologists and two dental residents.
Results: The diagnostic performance of the deep-learning system for maxillary sinusitis on panoramic radiographs was high, with accuracy of 87.5%, sensitivity of 86.7%, specificity of 88.3%, and AUC of 0.875. These values showed no significant differences compared with those of the radiologists and were higher than those of the dental residents.
Conclusions: The diagnostic performance of the deep-learning system for maxillary sinusitis on panoramic radiographs was sufficiently high. Results from the deep-learning system are expected to provide diagnostic support for inexperienced dentists.

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