A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography

The distal root of the mandibular first molar occasionally has an extra root, which can directly affect the outcome of endodontic therapy. In this study, we examined the diagnostic performance of a deep learning system for classification of the root morphology of mandibular first molars on panoramic...

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Veröffentlicht in:	Dento-maxillo-facial radiology Jg. 48; H. 3; S. 20180218
Hauptverfasser:	Hiraiwa, Teruhiko, Ariji, Yoshiko, Fukuda, Motoki, Kise, Yoshitaka, Nakata, Kazuhiko, Katsumata, Akitoshi, Fujita, Hiroshi, Ariji, Eiichiro
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England The British Institute of Radiology 01.03.2019
Schlagworte:	Adolescent Adult Artificial Intelligence Cone-Beam Computed Tomography Dental Pulp Cavity Female Humans Male Mandible Middle Aged Molar - diagnostic imaging Radiography, Panoramic Retrospective Studies Tooth Root - diagnostic imaging Young Adult deep learning mandibular first molar panoramic radiography artificial intelligence root morphology
ISSN:	0250-832X, 1476-542X
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Abstract	The distal root of the mandibular first molar occasionally has an extra root, which can directly affect the outcome of endodontic therapy. In this study, we examined the diagnostic performance of a deep learning system for classification of the root morphology of mandibular first molars on panoramic radiographs. Dental cone-beam CT (CBCT) was used as the gold standard. CBCT images and panoramic radiographs of 760 mandibular first molars from 400 patients who had not undergone root canal treatments were analyzed. Distal roots were examined on CBCT images to determine the presence of a single or extra root. Image patches of the roots were segmented from panoramic radiographs and applied to a deep learning system, and its diagnostic performance in the classification of root morphplogy was examined. Extra roots were observed in 21.4% of distal roots on CBCT images. The deep learning system had diagnostic accuracy of 86.9% for the determination of whether distal roots were single or had extra roots. The deep learning system showed high accuracy in the differential diagnosis of a single or extra root in the distal roots of mandibular first molars.
AbstractList	The distal root of the mandibular first molar occasionally has an extra root, which can directly affect the outcome of endodontic therapy. In this study, we examined the diagnostic performance of a deep learning system for classification of the root morphology of mandibular first molars on panoramic radiographs. Dental cone-beam CT (CBCT) was used as the gold standard.OBJECTIVES:The distal root of the mandibular first molar occasionally has an extra root, which can directly affect the outcome of endodontic therapy. In this study, we examined the diagnostic performance of a deep learning system for classification of the root morphology of mandibular first molars on panoramic radiographs. Dental cone-beam CT (CBCT) was used as the gold standard.CBCT images and panoramic radiographs of 760 mandibular first molars from 400 patients who had not undergone root canal treatments were analyzed. Distal roots were examined on CBCT images to determine the presence of a single or extra root. Image patches of the roots were segmented from panoramic radiographs and applied to a deep learning system, and its diagnostic performance in the classification of root morphplogy was examined.METHODS:CBCT images and panoramic radiographs of 760 mandibular first molars from 400 patients who had not undergone root canal treatments were analyzed. Distal roots were examined on CBCT images to determine the presence of a single or extra root. Image patches of the roots were segmented from panoramic radiographs and applied to a deep learning system, and its diagnostic performance in the classification of root morphplogy was examined.Extra roots were observed in 21.4% of distal roots on CBCT images. The deep learning system had diagnostic accuracy of 86.9% for the determination of whether distal roots were single or had extra roots.RESULTS:Extra roots were observed in 21.4% of distal roots on CBCT images. The deep learning system had diagnostic accuracy of 86.9% for the determination of whether distal roots were single or had extra roots.The deep learning system showed high accuracy in the differential diagnosis of a single or extra root in the distal roots of mandibular first molars.CONCLUSIONS:The deep learning system showed high accuracy in the differential diagnosis of a single or extra root in the distal roots of mandibular first molars. The distal root of the mandibular first molar occasionally has an extra root, which can directly affect the outcome of endodontic therapy. In this study, we examined the diagnostic performance of a deep learning system for classification of the root morphology of mandibular first molars on panoramic radiographs. Dental cone-beam CT (CBCT) was used as the gold standard. CBCT images and panoramic radiographs of 760 mandibular first molars from 400 patients who had not undergone root canal treatments were analyzed. Distal roots were examined on CBCT images to determine the presence of a single or extra root. Image patches of the roots were segmented from panoramic radiographs and applied to a deep learning system, and its diagnostic performance in the classification of root morphplogy was examined. Extra roots were observed in 21.4% of distal roots on CBCT images. The deep learning system had diagnostic accuracy of 86.9% for the determination of whether distal roots were single or had extra roots. The deep learning system showed high accuracy in the differential diagnosis of a single or extra root in the distal roots of mandibular first molars.
Author	Hiraiwa, Teruhiko Kise, Yoshitaka Katsumata, Akitoshi Nakata, Kazuhiko Ariji, Eiichiro Fujita, Hiroshi Fukuda, Motoki Ariji, Yoshiko
Author_xml	– sequence: 1 givenname: Teruhiko surname: Hiraiwa fullname: Hiraiwa, Teruhiko organization: Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, Nagoya, Japan – sequence: 2 givenname: Yoshiko surname: Ariji fullname: Ariji, Yoshiko organization: Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, Nagoya, Japan – sequence: 3 givenname: Motoki surname: Fukuda fullname: Fukuda, Motoki organization: Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, Nagoya, Japan – sequence: 4 givenname: Yoshitaka surname: Kise fullname: Kise, Yoshitaka organization: Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, Nagoya, Japan – sequence: 5 givenname: Kazuhiko surname: Nakata fullname: Nakata, Kazuhiko organization: Department of Endodontics, Aichi-Gakuin University School of Dentistry, Nagoya, Japan – sequence: 6 givenname: Akitoshi surname: Katsumata fullname: Katsumata, Akitoshi organization: Department of Oral Radiology, Asahi University School of Dentistry, Mizuho, Japan – sequence: 7 givenname: Hiroshi surname: Fujita fullname: Fujita, Hiroshi organization: Department of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu, Japan – sequence: 8 givenname: Eiichiro surname: Ariji fullname: Ariji, Eiichiro organization: Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, Nagoya, Japan
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SubjectTerms	Adolescent Adult Artificial Intelligence Cone-Beam Computed Tomography Dental Pulp Cavity Female Humans Male Mandible Middle Aged Molar - diagnostic imaging Radiography, Panoramic Retrospective Studies Tooth Root - diagnostic imaging Young Adult
Title	A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography
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