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

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Abstrakt

Tytuł:: Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography.
Autorzy:: Fukuda M; Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan. .
Inamoto K; Department of Endodontics, Aichi-Gakuin University School of Dentistry, Nagoya, Japan.
Shibata N; Department of Endodontics, Aichi-Gakuin University School of Dentistry, Nagoya, 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.
Yanashita Y; Department of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu, Japan.
Kutsuna S; Department of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu, Japan.
Nakata K; Department of Endodontics, Aichi-Gakuin University School of Dentistry, Nagoya, Japan.
Katsumata A; Department of Oral Radiology, Asahi University, 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.
Źródło:: Oral radiology [Oral Radiol] 2020 Oct; Vol. 36 (4), pp. 337-343. Date of Electronic Publication: 2019 Sep 18.
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:: Tooth Fractures*/diagnostic imaging
Artificial Intelligence ; Cone-Beam Computed Tomography ; Humans ; Radiography, Panoramic ; Reproducibility of Results ; Tooth Root/diagnostic imaging
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Contributed Indexing:: Keywords: Artificial intelligence; Deep learning; Object detection; Panoramic radiography; Vertical root fracture
Entry Date(s):: Date Created: 20190920 Date Completed: 20201126 Latest Revision: 20220419
Update Code:: 20240105
DOI:: 10.1007/s11282-019-00409-x
PMID:: 31535278
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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.

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