Automatic ganglion cell detection for improving the efficiency and accuracy of hirschprung disease diagnosis

Abstract Histopathologic diagnosis of Hirschsprung's disease (HSCR) is time consuming and requires expertise. The use of artificial intelligence (AI) in digital pathology is actively researched and may improve the diagnosis of HSCR. The purpose of this research was to develop an algorithm capab...

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Autores principales:	Ariel Greenberg, Asaf Aizic, Asia Zubkov, Sarah Borsekofsky, Rami R. Hagege, Dov Hershkovitz
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8ca5719ee4a04a26979e03abc344c30e
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spelling	oai:doaj.org-article:8ca5719ee4a04a26979e03abc344c30e2021-12-02T14:26:46ZAutomatic ganglion cell detection for improving the efficiency and accuracy of hirschprung disease diagnosis10.1038/s41598-021-82869-y2045-2322https://doaj.org/article/8ca5719ee4a04a26979e03abc344c30e2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82869-yhttps://doaj.org/toc/2045-2322Abstract Histopathologic diagnosis of Hirschsprung's disease (HSCR) is time consuming and requires expertise. The use of artificial intelligence (AI) in digital pathology is actively researched and may improve the diagnosis of HSCR. The purpose of this research was to develop an algorithm capable of identifying ganglion cells in digital pathology slides and implement it as an assisting tool for the pathologist in the diagnosis of HSCR. Ninety five digital pathology slides were used for the construction and training of the algorithm. Fifty cases suspected for HSCR (727 slides) were used as a validation cohort. Image sets suspected to contain ganglion cells were chosen by the algorithm and then reviewed and scored by five pathologists, one HSCR expert and 4 non-experts. The algorithm was able to identify ganglion cells with 96% sensitivity and 99% specificity (in normal colon) as well as to correctly identify a case previously misdiagnosed as non-HSCR. The expert was able to achieve perfectly accurate diagnoses based solely on the images suggested by the algorithm, with over 95% time saved. Non-experts would require expert consultation in 20–58% of the cases to achieve similar results. The use of AI in the diagnosis of HSCR can greatly reduce the time and effort required for diagnosis and improve accuracy.Ariel GreenbergAsaf AizicAsia ZubkovSarah BorsekofskyRami R. HagegeDov HershkovitzNature 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 Ariel Greenberg Asaf Aizic Asia Zubkov Sarah Borsekofsky Rami R. Hagege Dov Hershkovitz Automatic ganglion cell detection for improving the efficiency and accuracy of hirschprung disease diagnosis
description	Abstract Histopathologic diagnosis of Hirschsprung's disease (HSCR) is time consuming and requires expertise. The use of artificial intelligence (AI) in digital pathology is actively researched and may improve the diagnosis of HSCR. The purpose of this research was to develop an algorithm capable of identifying ganglion cells in digital pathology slides and implement it as an assisting tool for the pathologist in the diagnosis of HSCR. Ninety five digital pathology slides were used for the construction and training of the algorithm. Fifty cases suspected for HSCR (727 slides) were used as a validation cohort. Image sets suspected to contain ganglion cells were chosen by the algorithm and then reviewed and scored by five pathologists, one HSCR expert and 4 non-experts. The algorithm was able to identify ganglion cells with 96% sensitivity and 99% specificity (in normal colon) as well as to correctly identify a case previously misdiagnosed as non-HSCR. The expert was able to achieve perfectly accurate diagnoses based solely on the images suggested by the algorithm, with over 95% time saved. Non-experts would require expert consultation in 20–58% of the cases to achieve similar results. The use of AI in the diagnosis of HSCR can greatly reduce the time and effort required for diagnosis and improve accuracy.
format	article
author	Ariel Greenberg Asaf Aizic Asia Zubkov Sarah Borsekofsky Rami R. Hagege Dov Hershkovitz
author_facet	Ariel Greenberg Asaf Aizic Asia Zubkov Sarah Borsekofsky Rami R. Hagege Dov Hershkovitz
author_sort	Ariel Greenberg
title	Automatic ganglion cell detection for improving the efficiency and accuracy of hirschprung disease diagnosis
title_short	Automatic ganglion cell detection for improving the efficiency and accuracy of hirschprung disease diagnosis
title_full	Automatic ganglion cell detection for improving the efficiency and accuracy of hirschprung disease diagnosis
title_fullStr	Automatic ganglion cell detection for improving the efficiency and accuracy of hirschprung disease diagnosis
title_full_unstemmed	Automatic ganglion cell detection for improving the efficiency and accuracy of hirschprung disease diagnosis
title_sort	automatic ganglion cell detection for improving the efficiency and accuracy of hirschprung disease diagnosis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8ca5719ee4a04a26979e03abc344c30e
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Automatic ganglion cell detection for improving the efficiency and accuracy of hirschprung disease diagnosis

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