Deep learning neural networks to differentiate Stafne's bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography.

This study aimed to develop a high-performance deep learning algorithm to differentiate Stafne's bone cavity (SBC) from cysts and tumors of the jaw based on images acquired from various panoramic radiographic systems. Data sets included 176 Stafne's bone cavities and 282 odontogenic cysts...

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Autores principales:	Ari Lee, Min Su Kim, Sang-Sun Han, PooGyeon Park, Chena Lee, Jong Pil Yun
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e1f70e53393f401eb2d53d1f62db930a
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spelling	oai:doaj.org-article:e1f70e53393f401eb2d53d1f62db930a2021-12-02T20:06:46ZDeep learning neural networks to differentiate Stafne's bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography.1932-620310.1371/journal.pone.0254997https://doaj.org/article/e1f70e53393f401eb2d53d1f62db930a2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254997https://doaj.org/toc/1932-6203This study aimed to develop a high-performance deep learning algorithm to differentiate Stafne's bone cavity (SBC) from cysts and tumors of the jaw based on images acquired from various panoramic radiographic systems. Data sets included 176 Stafne's bone cavities and 282 odontogenic cysts and tumors of the mandible (98 dentigerous cysts, 91 odontogenic keratocysts, and 93 ameloblastomas) that required surgical removal. Panoramic radiographs were obtained using three different imaging systems. The trained model showed 99.25% accuracy, 98.08% sensitivity, and 100% specificity for SBC classification and resulted in one misclassified SBC case. The algorithm was approved to recognize the typical imaging features of SBC in panoramic radiography regardless of the imaging system when traced back with Grad-Cam and Guided Grad-Cam methods. The deep learning model for SBC differentiating from odontogenic cysts and tumors showed high performance with images obtained from multiple panoramic systems. The present algorithm is expected to be a useful tool for clinicians, as it diagnoses SBCs in panoramic radiography to prevent unnecessary examinations for patients. Additionally, it would provide support for clinicians to determine further examinations or referrals to surgeons for cases where even experts are unsure of diagnosis using panoramic radiography alone.Ari LeeMin Su KimSang-Sun HanPooGyeon ParkChena LeeJong Pil YunPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254997 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ari Lee Min Su Kim Sang-Sun Han PooGyeon Park Chena Lee Jong Pil Yun Deep learning neural networks to differentiate Stafne's bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography.
description	This study aimed to develop a high-performance deep learning algorithm to differentiate Stafne's bone cavity (SBC) from cysts and tumors of the jaw based on images acquired from various panoramic radiographic systems. Data sets included 176 Stafne's bone cavities and 282 odontogenic cysts and tumors of the mandible (98 dentigerous cysts, 91 odontogenic keratocysts, and 93 ameloblastomas) that required surgical removal. Panoramic radiographs were obtained using three different imaging systems. The trained model showed 99.25% accuracy, 98.08% sensitivity, and 100% specificity for SBC classification and resulted in one misclassified SBC case. The algorithm was approved to recognize the typical imaging features of SBC in panoramic radiography regardless of the imaging system when traced back with Grad-Cam and Guided Grad-Cam methods. The deep learning model for SBC differentiating from odontogenic cysts and tumors showed high performance with images obtained from multiple panoramic systems. The present algorithm is expected to be a useful tool for clinicians, as it diagnoses SBCs in panoramic radiography to prevent unnecessary examinations for patients. Additionally, it would provide support for clinicians to determine further examinations or referrals to surgeons for cases where even experts are unsure of diagnosis using panoramic radiography alone.
format	article
author	Ari Lee Min Su Kim Sang-Sun Han PooGyeon Park Chena Lee Jong Pil Yun
author_facet	Ari Lee Min Su Kim Sang-Sun Han PooGyeon Park Chena Lee Jong Pil Yun
author_sort	Ari Lee
title	Deep learning neural networks to differentiate Stafne's bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography.
title_short	Deep learning neural networks to differentiate Stafne's bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography.
title_full	Deep learning neural networks to differentiate Stafne's bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography.
title_fullStr	Deep learning neural networks to differentiate Stafne's bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography.
title_full_unstemmed	Deep learning neural networks to differentiate Stafne's bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography.
title_sort	deep learning neural networks to differentiate stafne's bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/e1f70e53393f401eb2d53d1f62db930a
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Deep learning neural networks to differentiate Stafne's bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography.

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