Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram

Abstract Orthopantomogram (OPG) is important for primary diagnosis of temporomandibular joint osteoarthritis (TMJOA), because of cost and the radiation associated with computed tomograms (CT). The aims of this study were to develop an artificial intelligence (AI) model and compare its TMJOA diagnost...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Eunhye Choi, Donghyun Kim, Jeong-Yun Lee, Hee-Kyung Park
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0ef6c1e8f22646428af26f5ae997cd3d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0ef6c1e8f22646428af26f5ae997cd3d
record_format	dspace
spelling	oai:doaj.org-article:0ef6c1e8f22646428af26f5ae997cd3d2021-12-02T15:55:18ZArtificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram10.1038/s41598-021-89742-y2045-2322https://doaj.org/article/0ef6c1e8f22646428af26f5ae997cd3d2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89742-yhttps://doaj.org/toc/2045-2322Abstract Orthopantomogram (OPG) is important for primary diagnosis of temporomandibular joint osteoarthritis (TMJOA), because of cost and the radiation associated with computed tomograms (CT). The aims of this study were to develop an artificial intelligence (AI) model and compare its TMJOA diagnostic performance from OPGs with that of an oromaxillofacial radiology (OMFR) expert. An AI model was developed using Karas’ ResNet model and trained to classify images into three categories: normal, indeterminate OA, and OA. This study included 1189 OPG images confirmed by cone-beam CT and evaluated the results by model (accuracy, precision, recall, and F1 score) and diagnostic performance (accuracy, sensitivity, and specificity). The model performance was unsatisfying when AI was developed with 3 categories. After the indeterminate OA images were reclassified as normal, OA, or omission, the AI diagnosed TMJOA in a similar manner to an expert and was in most accord with CBCT when the indeterminate OA category was omitted (accuracy: 0.78, sensitivity: 0.73, and specificity: 0.82). Our deep learning model showed a sensitivity equivalent to that of an expert, with a better balance between sensitivity and specificity, which implies that AI can play an important role in primary diagnosis of TMJOA from OPGs in most general practice clinics where OMFR experts or CT are not available.Eunhye ChoiDonghyun KimJeong-Yun LeeHee-Kyung ParkNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Eunhye Choi Donghyun Kim Jeong-Yun Lee Hee-Kyung Park Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram
description	Abstract Orthopantomogram (OPG) is important for primary diagnosis of temporomandibular joint osteoarthritis (TMJOA), because of cost and the radiation associated with computed tomograms (CT). The aims of this study were to develop an artificial intelligence (AI) model and compare its TMJOA diagnostic performance from OPGs with that of an oromaxillofacial radiology (OMFR) expert. An AI model was developed using Karas’ ResNet model and trained to classify images into three categories: normal, indeterminate OA, and OA. This study included 1189 OPG images confirmed by cone-beam CT and evaluated the results by model (accuracy, precision, recall, and F1 score) and diagnostic performance (accuracy, sensitivity, and specificity). The model performance was unsatisfying when AI was developed with 3 categories. After the indeterminate OA images were reclassified as normal, OA, or omission, the AI diagnosed TMJOA in a similar manner to an expert and was in most accord with CBCT when the indeterminate OA category was omitted (accuracy: 0.78, sensitivity: 0.73, and specificity: 0.82). Our deep learning model showed a sensitivity equivalent to that of an expert, with a better balance between sensitivity and specificity, which implies that AI can play an important role in primary diagnosis of TMJOA from OPGs in most general practice clinics where OMFR experts or CT are not available.
format	article
author	Eunhye Choi Donghyun Kim Jeong-Yun Lee Hee-Kyung Park
author_facet	Eunhye Choi Donghyun Kim Jeong-Yun Lee Hee-Kyung Park
author_sort	Eunhye Choi
title	Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram
title_short	Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram
title_full	Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram
title_fullStr	Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram
title_full_unstemmed	Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram
title_sort	artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0ef6c1e8f22646428af26f5ae997cd3d
work_keys_str_mv	AT eunhyechoi artificialintelligenceindetectingtemporomandibularjointosteoarthritisonorthopantomogram AT donghyunkim artificialintelligenceindetectingtemporomandibularjointosteoarthritisonorthopantomogram AT jeongyunlee artificialintelligenceindetectingtemporomandibularjointosteoarthritisonorthopantomogram AT heekyungpark artificialintelligenceindetectingtemporomandibularjointosteoarthritisonorthopantomogram
_version_	1718385372277243904

Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram

Ejemplares similares