Deep learning based prediction of extraction difficulty for mandibular third molars

Abstract This paper proposes a convolutional neural network (CNN)-based deep learning model for predicting the difficulty of extracting a mandibular third molar using a panoramic radiographic image. The applied dataset includes a total of 1053 mandibular third molars from 600 preoperative panoramic...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jeong-Hun Yoo, Han-Gyeol Yeom, WooSang Shin, Jong Pil Yun, Jong Hyun Lee, Seung Hyun Jeong, Hun Jun Lim, Jun Lee, Bong Chul Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ca8290bdccde45128660e9a4deb20ddd
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ca8290bdccde45128660e9a4deb20ddd
record_format	dspace
spelling	oai:doaj.org-article:ca8290bdccde45128660e9a4deb20ddd2021-12-02T13:48:41ZDeep learning based prediction of extraction difficulty for mandibular third molars10.1038/s41598-021-81449-42045-2322https://doaj.org/article/ca8290bdccde45128660e9a4deb20ddd2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81449-4https://doaj.org/toc/2045-2322Abstract This paper proposes a convolutional neural network (CNN)-based deep learning model for predicting the difficulty of extracting a mandibular third molar using a panoramic radiographic image. The applied dataset includes a total of 1053 mandibular third molars from 600 preoperative panoramic radiographic images. The extraction difficulty was evaluated based on the consensus of three human observers using the Pederson difficulty score (PDS). The classification model used a ResNet-34 pretrained on the ImageNet dataset. The correlation between the PDS values determined by the proposed model and those measured by the experts was calculated. The prediction accuracies for C1 (depth), C2 (ramal relationship), and C3 (angulation) were 78.91%, 82.03%, and 90.23%, respectively. The results confirm that the proposed CNN-based deep learning model could be used to predict the difficulty of extracting a mandibular third molar using a panoramic radiographic image.Jeong-Hun YooHan-Gyeol YeomWooSang ShinJong Pil YunJong Hyun LeeSeung Hyun JeongHun Jun LimJun LeeBong Chul KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jeong-Hun Yoo Han-Gyeol Yeom WooSang Shin Jong Pil Yun Jong Hyun Lee Seung Hyun Jeong Hun Jun Lim Jun Lee Bong Chul Kim Deep learning based prediction of extraction difficulty for mandibular third molars
description	Abstract This paper proposes a convolutional neural network (CNN)-based deep learning model for predicting the difficulty of extracting a mandibular third molar using a panoramic radiographic image. The applied dataset includes a total of 1053 mandibular third molars from 600 preoperative panoramic radiographic images. The extraction difficulty was evaluated based on the consensus of three human observers using the Pederson difficulty score (PDS). The classification model used a ResNet-34 pretrained on the ImageNet dataset. The correlation between the PDS values determined by the proposed model and those measured by the experts was calculated. The prediction accuracies for C1 (depth), C2 (ramal relationship), and C3 (angulation) were 78.91%, 82.03%, and 90.23%, respectively. The results confirm that the proposed CNN-based deep learning model could be used to predict the difficulty of extracting a mandibular third molar using a panoramic radiographic image.
format	article
author	Jeong-Hun Yoo Han-Gyeol Yeom WooSang Shin Jong Pil Yun Jong Hyun Lee Seung Hyun Jeong Hun Jun Lim Jun Lee Bong Chul Kim
author_facet	Jeong-Hun Yoo Han-Gyeol Yeom WooSang Shin Jong Pil Yun Jong Hyun Lee Seung Hyun Jeong Hun Jun Lim Jun Lee Bong Chul Kim
author_sort	Jeong-Hun Yoo
title	Deep learning based prediction of extraction difficulty for mandibular third molars
title_short	Deep learning based prediction of extraction difficulty for mandibular third molars
title_full	Deep learning based prediction of extraction difficulty for mandibular third molars
title_fullStr	Deep learning based prediction of extraction difficulty for mandibular third molars
title_full_unstemmed	Deep learning based prediction of extraction difficulty for mandibular third molars
title_sort	deep learning based prediction of extraction difficulty for mandibular third molars
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ca8290bdccde45128660e9a4deb20ddd
work_keys_str_mv	AT jeonghunyoo deeplearningbasedpredictionofextractiondifficultyformandibularthirdmolars AT hangyeolyeom deeplearningbasedpredictionofextractiondifficultyformandibularthirdmolars AT woosangshin deeplearningbasedpredictionofextractiondifficultyformandibularthirdmolars AT jongpilyun deeplearningbasedpredictionofextractiondifficultyformandibularthirdmolars AT jonghyunlee deeplearningbasedpredictionofextractiondifficultyformandibularthirdmolars AT seunghyunjeong deeplearningbasedpredictionofextractiondifficultyformandibularthirdmolars AT hunjunlim deeplearningbasedpredictionofextractiondifficultyformandibularthirdmolars AT junlee deeplearningbasedpredictionofextractiondifficultyformandibularthirdmolars AT bongchulkim deeplearningbasedpredictionofextractiondifficultyformandibularthirdmolars
_version_	1718392465427267584

Deep learning based prediction of extraction difficulty for mandibular third molars

Ejemplares similares