Cargando…
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)...
Autores principales: | , , , , , , , , , , , , |
---|---|
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 |
_version_ | 1783558531512795136 |
---|---|
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. |
format | Online Article Text |
id | pubmed-7356620 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT yanghyunwoo deeplearningforautomateddetectionofcystandtumorsofthejawinpanoramicradiographs AT joeun deeplearningforautomateddetectionofcystandtumorsofthejawinpanoramicradiographs AT kimhyungjun deeplearningforautomateddetectionofcystandtumorsofthejawinpanoramicradiographs AT chainho deeplearningforautomateddetectionofcystandtumorsofthejawinpanoramicradiographs AT jungyoungsoo deeplearningforautomateddetectionofcystandtumorsofthejawinpanoramicradiographs AT namwoong deeplearningforautomateddetectionofcystandtumorsofthejawinpanoramicradiographs AT kimjunyoung deeplearningforautomateddetectionofcystandtumorsofthejawinpanoramicradiographs AT kimjinkyu deeplearningforautomateddetectionofcystandtumorsofthejawinpanoramicradiographs AT kimyoonhyeon deeplearningforautomateddetectionofcystandtumorsofthejawinpanoramicradiographs AT ohtaegyeong deeplearningforautomateddetectionofcystandtumorsofthejawinpanoramicradiographs AT hansangsun deeplearningforautomateddetectionofcystandtumorsofthejawinpanoramicradiographs AT kimhwiyoung deeplearningforautomateddetectionofcystandtumorsofthejawinpanoramicradiographs AT kimdongwook deeplearningforautomateddetectionofcystandtumorsofthejawinpanoramicradiographs |