Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography

Objectives The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for detecting vertical root fracture (VRF) on panoramic radiography. Methods Three hundred panoramic images containing a total of 330 VRF teeth with clearly visible fracture lines were selected fr...

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Vydáno v:	Oral radiology Ročník 36; číslo 4; s. 337 - 343
Hlavní autoři:	Fukuda, Motoki, Inamoto, Kyoko, Shibata, Naoki, Ariji, Yoshiko, Yanashita, Yudai, Kutsuna, Shota, Nakata, Kazuhiko, Katsumata, Akitoshi, Fujita, Hiroshi, Ariji, Eiichiro
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Singapore Springer Singapore 01.10.2020 Springer Nature B.V
Témata:	Artificial Intelligence Cone-Beam Computed Tomography Deep learning Dentistry Humans Imaging Medicine Neural networks Oral and Maxillofacial Surgery Original Article Radiography Radiography, Panoramic Radiology Reproducibility of Results Teeth Tooth Fractures - diagnostic imaging Tooth Root - diagnostic imaging Deep learning Vertical root fracture Panoramic radiography Artificial intelligence Object detection
ISSN:	0911-6028, 1613-9674, 1613-9674
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Abstract	Objectives The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for detecting vertical root fracture (VRF) on panoramic radiography. Methods Three hundred panoramic images containing a total of 330 VRF teeth with clearly visible fracture lines were selected from our hospital imaging database. Confirmation of VRF lines was performed by two radiologists and one endodontist. Eighty percent (240 images) of the 300 images were assigned to a training set and 20% (60 images) to a test set. A CNN-based deep learning model for the detection of VRFs was built using DetectNet with DIGITS version 5.0. To defend test data selection bias and increase reliability, fivefold cross-validation was performed. Diagnostic performance was evaluated using recall, precision, and F measure. Results Of the 330 VRFs, 267 were detected. Twenty teeth without fractures were falsely detected. Recall was 0.75, precision 0.93, and F measure 0.83. Conclusions The CNN learning model has shown promise as a tool to detect VRFs on panoramic images and to function as a CAD tool.
AbstractList	Objectives The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for detecting vertical root fracture (VRF) on panoramic radiography. Methods Three hundred panoramic images containing a total of 330 VRF teeth with clearly visible fracture lines were selected from our hospital imaging database. Confirmation of VRF lines was performed by two radiologists and one endodontist. Eighty percent (240 images) of the 300 images were assigned to a training set and 20% (60 images) to a test set. A CNN-based deep learning model for the detection of VRFs was built using DetectNet with DIGITS version 5.0. To defend test data selection bias and increase reliability, fivefold cross-validation was performed. Diagnostic performance was evaluated using recall, precision, and F measure. Results Of the 330 VRFs, 267 were detected. Twenty teeth without fractures were falsely detected. Recall was 0.75, precision 0.93, and F measure 0.83. Conclusions The CNN learning model has shown promise as a tool to detect VRFs on panoramic images and to function as a CAD tool. ObjectivesThe aim of this study was to evaluate the use of a convolutional neural network (CNN) system for detecting vertical root fracture (VRF) on panoramic radiography.MethodsThree hundred panoramic images containing a total of 330 VRF teeth with clearly visible fracture lines were selected from our hospital imaging database. Confirmation of VRF lines was performed by two radiologists and one endodontist. Eighty percent (240 images) of the 300 images were assigned to a training set and 20% (60 images) to a test set. A CNN-based deep learning model for the detection of VRFs was built using DetectNet with DIGITS version 5.0. To defend test data selection bias and increase reliability, fivefold cross-validation was performed. Diagnostic performance was evaluated using recall, precision, and F measure.ResultsOf the 330 VRFs, 267 were detected. Twenty teeth without fractures were falsely detected. Recall was 0.75, precision 0.93, and F measure 0.83.ConclusionsThe CNN learning model has shown promise as a tool to detect VRFs on panoramic images and to function as a CAD tool. The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for detecting vertical root fracture (VRF) on panoramic radiography. Three hundred panoramic images containing a total of 330 VRF teeth with clearly visible fracture lines were selected from our hospital imaging database. Confirmation of VRF lines was performed by two radiologists and one endodontist. Eighty percent (240 images) of the 300 images were assigned to a training set and 20% (60 images) to a test set. A CNN-based deep learning model for the detection of VRFs was built using DetectNet with DIGITS version 5.0. To defend test data selection bias and increase reliability, fivefold cross-validation was performed. Diagnostic performance was evaluated using recall, precision, and F measure. Of the 330 VRFs, 267 were detected. Twenty teeth without fractures were falsely detected. Recall was 0.75, precision 0.93, and F measure 0.83. The CNN learning model has shown promise as a tool to detect VRFs on panoramic images and to function as a CAD tool. The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for detecting vertical root fracture (VRF) on panoramic radiography.OBJECTIVESThe aim of this study was to evaluate the use of a convolutional neural network (CNN) system for detecting vertical root fracture (VRF) on panoramic radiography.Three hundred panoramic images containing a total of 330 VRF teeth with clearly visible fracture lines were selected from our hospital imaging database. Confirmation of VRF lines was performed by two radiologists and one endodontist. Eighty percent (240 images) of the 300 images were assigned to a training set and 20% (60 images) to a test set. A CNN-based deep learning model for the detection of VRFs was built using DetectNet with DIGITS version 5.0. To defend test data selection bias and increase reliability, fivefold cross-validation was performed. Diagnostic performance was evaluated using recall, precision, and F measure.METHODSThree hundred panoramic images containing a total of 330 VRF teeth with clearly visible fracture lines were selected from our hospital imaging database. Confirmation of VRF lines was performed by two radiologists and one endodontist. Eighty percent (240 images) of the 300 images were assigned to a training set and 20% (60 images) to a test set. A CNN-based deep learning model for the detection of VRFs was built using DetectNet with DIGITS version 5.0. To defend test data selection bias and increase reliability, fivefold cross-validation was performed. Diagnostic performance was evaluated using recall, precision, and F measure.Of the 330 VRFs, 267 were detected. Twenty teeth without fractures were falsely detected. Recall was 0.75, precision 0.93, and F measure 0.83.RESULTSOf the 330 VRFs, 267 were detected. Twenty teeth without fractures were falsely detected. Recall was 0.75, precision 0.93, and F measure 0.83.The CNN learning model has shown promise as a tool to detect VRFs on panoramic images and to function as a CAD tool.CONCLUSIONSThe CNN learning model has shown promise as a tool to detect VRFs on panoramic images and to function as a CAD tool.
Author	Katsumata, Akitoshi Kutsuna, Shota Inamoto, Kyoko Nakata, Kazuhiko Ariji, Eiichiro Fujita, Hiroshi Yanashita, Yudai Fukuda, Motoki Shibata, Naoki Ariji, Yoshiko
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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31535278$$D View this record in MEDLINE/PubMed
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ContentType	Journal Article
Copyright	Japanese Society for Oral and Maxillofacial Radiology and Springer Nature Singapore Pte Ltd. 2019 Oral Radiology is a copyright of Springer, (2019). All Rights Reserved. Japanese Society for Oral and Maxillofacial Radiology and Springer Nature Singapore Pte Ltd. 2019.
Copyright_xml	– notice: Japanese Society for Oral and Maxillofacial Radiology and Springer Nature Singapore Pte Ltd. 2019 – notice: Oral Radiology is a copyright of Springer, (2019). All Rights Reserved. – notice: Japanese Society for Oral and Maxillofacial Radiology and Springer Nature Singapore Pte Ltd. 2019.
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Keywords	Deep learning Vertical root fracture Panoramic radiography Artificial intelligence Object detection
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PublicationTitle	Oral radiology
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Snippet	Objectives The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for detecting vertical root fracture (VRF) on panoramic... The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for detecting vertical root fracture (VRF) on panoramic... ObjectivesThe aim of this study was to evaluate the use of a convolutional neural network (CNN) system for detecting vertical root fracture (VRF) on panoramic...
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SubjectTerms	Artificial Intelligence Cone-Beam Computed Tomography Deep learning Dentistry Humans Imaging Medicine Neural networks Oral and Maxillofacial Surgery Original Article Radiography Radiography, Panoramic Radiology Reproducibility of Results Teeth Tooth Fractures - diagnostic imaging Tooth Root - diagnostic imaging
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Title	Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography
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