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Deep Learning for Automated Detection of Cyst and Tumors of the Jaw in Panoramic Radiographs

Patients with odontogenic cysts and tumors may have to undergo serious surgery unless the lesion is properly detected at the early stage. The purpose of this study is to evaluate the diagnostic performance of the real-time object detecting deep convolutional neural network You Only Look Once (YOLO)...

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Autores principales: Yang, Hyunwoo, Jo, Eun, Kim, Hyung Jun, Cha, In-ho, Jung, Young-Soo, Nam, Woong, Kim, Jun-Young, Kim, Jin-Kyu, Kim, Yoon Hyeon, Oh, Tae Gyeong, Han, Sang-Sun, Kim, Hwiyoung, Kim, Dongwook
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7356620/
https://www.ncbi.nlm.nih.gov/pubmed/32545602
http://dx.doi.org/10.3390/jcm9061839
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author Yang, Hyunwoo
Jo, Eun
Kim, Hyung Jun
Cha, In-ho
Jung, Young-Soo
Nam, Woong
Kim, Jun-Young
Kim, Jin-Kyu
Kim, Yoon Hyeon
Oh, Tae Gyeong
Han, Sang-Sun
Kim, Hwiyoung
Kim, Dongwook
author_facet Yang, Hyunwoo
Jo, Eun
Kim, Hyung Jun
Cha, In-ho
Jung, Young-Soo
Nam, Woong
Kim, Jun-Young
Kim, Jin-Kyu
Kim, Yoon Hyeon
Oh, Tae Gyeong
Han, Sang-Sun
Kim, Hwiyoung
Kim, Dongwook
author_sort Yang, Hyunwoo
collection PubMed
description Patients with odontogenic cysts and tumors may have to undergo serious surgery unless the lesion is properly detected at the early stage. The purpose of this study is to evaluate the diagnostic performance of the real-time object detecting deep convolutional neural network You Only Look Once (YOLO) v2—a deep learning algorithm that can both detect and classify an object at the same time—on panoramic radiographs. In this study, 1602 lesions on panoramic radiographs taken from 2010 to 2019 at Yonsei University Dental Hospital were selected as a database. Images were classified and labeled into four categories: dentigerous cysts, odontogenic keratocyst, ameloblastoma, and no lesion. Comparative analysis among three groups (YOLO, oral and maxillofacial surgeons, and general practitioners) was done in terms of precision, recall, accuracy, and F1 score. While YOLO ranked highest among the three groups (precision = 0.707, recall = 0.680), the performance differences between the machine and clinicians were statistically insignificant. The results of this study indicate the usefulness of auto-detecting convolutional networks in certain pathology detection and thus morbidity prevention in the field of oral and maxillofacial surgery.
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spelling pubmed-73566202020-07-22 Deep Learning for Automated Detection of Cyst and Tumors of the Jaw in Panoramic Radiographs Yang, Hyunwoo Jo, Eun Kim, Hyung Jun Cha, In-ho Jung, Young-Soo Nam, Woong Kim, Jun-Young Kim, Jin-Kyu Kim, Yoon Hyeon Oh, Tae Gyeong Han, Sang-Sun Kim, Hwiyoung Kim, Dongwook J Clin Med Article Patients with odontogenic cysts and tumors may have to undergo serious surgery unless the lesion is properly detected at the early stage. The purpose of this study is to evaluate the diagnostic performance of the real-time object detecting deep convolutional neural network You Only Look Once (YOLO) v2—a deep learning algorithm that can both detect and classify an object at the same time—on panoramic radiographs. In this study, 1602 lesions on panoramic radiographs taken from 2010 to 2019 at Yonsei University Dental Hospital were selected as a database. Images were classified and labeled into four categories: dentigerous cysts, odontogenic keratocyst, ameloblastoma, and no lesion. Comparative analysis among three groups (YOLO, oral and maxillofacial surgeons, and general practitioners) was done in terms of precision, recall, accuracy, and F1 score. While YOLO ranked highest among the three groups (precision = 0.707, recall = 0.680), the performance differences between the machine and clinicians were statistically insignificant. The results of this study indicate the usefulness of auto-detecting convolutional networks in certain pathology detection and thus morbidity prevention in the field of oral and maxillofacial surgery. MDPI 2020-06-12 /pmc/articles/PMC7356620/ /pubmed/32545602 http://dx.doi.org/10.3390/jcm9061839 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yang, Hyunwoo
Jo, Eun
Kim, Hyung Jun
Cha, In-ho
Jung, Young-Soo
Nam, Woong
Kim, Jun-Young
Kim, Jin-Kyu
Kim, Yoon Hyeon
Oh, Tae Gyeong
Han, Sang-Sun
Kim, Hwiyoung
Kim, Dongwook
Deep Learning for Automated Detection of Cyst and Tumors of the Jaw in Panoramic Radiographs
title Deep Learning for Automated Detection of Cyst and Tumors of the Jaw in Panoramic Radiographs
title_full Deep Learning for Automated Detection of Cyst and Tumors of the Jaw in Panoramic Radiographs
title_fullStr Deep Learning for Automated Detection of Cyst and Tumors of the Jaw in Panoramic Radiographs
title_full_unstemmed Deep Learning for Automated Detection of Cyst and Tumors of the Jaw in Panoramic Radiographs
title_short Deep Learning for Automated Detection of Cyst and Tumors of the Jaw in Panoramic Radiographs
title_sort deep learning for automated detection of cyst and tumors of the jaw in panoramic radiographs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7356620/
https://www.ncbi.nlm.nih.gov/pubmed/32545602
http://dx.doi.org/10.3390/jcm9061839
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