Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning

Recent literature has revealed a long discussion about the importance and necessity of nerve conduction studies in carpal tunnel syndrome management. The purpose of this study was to investigate the possibility of automatic detection, based on electrodiagnostic features, for the median nerve mononeu...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Konstantinos I. Tsamis, Prokopis Kontogiannis, Ioannis Gourgiotis, Stefanos Ntabos, Ioannis Sarmas, George Manis
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	carpal tunnel syndrome CTS feature extraction machine learning median nerve mononeuropathy nerve conduction studies Technology T Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/8462da5c4d0546169eb70b8145bcc595
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:8462da5c4d0546169eb70b8145bcc595
record_format	dspace
spelling	oai:doaj.org-article:8462da5c4d0546169eb70b8145bcc5952021-11-25T16:46:38ZAutomatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning10.3390/bioengineering81101812306-5354https://doaj.org/article/8462da5c4d0546169eb70b8145bcc5952021-11-01T00:00:00Zhttps://www.mdpi.com/2306-5354/8/11/181https://doaj.org/toc/2306-5354Recent literature has revealed a long discussion about the importance and necessity of nerve conduction studies in carpal tunnel syndrome management. The purpose of this study was to investigate the possibility of automatic detection, based on electrodiagnostic features, for the median nerve mononeuropathy and decision making about carpal tunnel syndrome. The study included 38 volunteers, examined prospectively. The purpose was to investigate the possibility of automatically detecting the median nerve mononeuropathy based on common electrodiagnostic criteria, used in everyday clinical practice, as well as new features selected based on physiology and mathematics. Machine learning techniques were used to combine the examined characteristics for a stable and accurate diagnosis. Automatic electrodiagnosis reached an accuracy of 95% compared to the standard neurophysiological diagnosis of the physicians with nerve conduction studies and 89% compared to the clinical diagnosis. The results show that the automatic detection of carpal tunnel syndrome is possible and can be employed in decision making, excluding human error. It is also shown that the novel features investigated can be used for the detection of the syndrome, complementary to the commonly used ones, increasing the accuracy of the method.Konstantinos I. TsamisProkopis KontogiannisIoannis GourgiotisStefanos NtabosIoannis SarmasGeorge ManisMDPI AGarticlecarpal tunnel syndromeCTSfeature extractionmachine learningmedian nerve mononeuropathynerve conduction studiesTechnologyTBiology (General)QH301-705.5ENBioengineering, Vol 8, Iss 181, p 181 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	carpal tunnel syndrome CTS feature extraction machine learning median nerve mononeuropathy nerve conduction studies Technology T Biology (General) QH301-705.5
spellingShingle	carpal tunnel syndrome CTS feature extraction machine learning median nerve mononeuropathy nerve conduction studies Technology T Biology (General) QH301-705.5 Konstantinos I. Tsamis Prokopis Kontogiannis Ioannis Gourgiotis Stefanos Ntabos Ioannis Sarmas George Manis Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning
description	Recent literature has revealed a long discussion about the importance and necessity of nerve conduction studies in carpal tunnel syndrome management. The purpose of this study was to investigate the possibility of automatic detection, based on electrodiagnostic features, for the median nerve mononeuropathy and decision making about carpal tunnel syndrome. The study included 38 volunteers, examined prospectively. The purpose was to investigate the possibility of automatically detecting the median nerve mononeuropathy based on common electrodiagnostic criteria, used in everyday clinical practice, as well as new features selected based on physiology and mathematics. Machine learning techniques were used to combine the examined characteristics for a stable and accurate diagnosis. Automatic electrodiagnosis reached an accuracy of 95% compared to the standard neurophysiological diagnosis of the physicians with nerve conduction studies and 89% compared to the clinical diagnosis. The results show that the automatic detection of carpal tunnel syndrome is possible and can be employed in decision making, excluding human error. It is also shown that the novel features investigated can be used for the detection of the syndrome, complementary to the commonly used ones, increasing the accuracy of the method.
format	article
author	Konstantinos I. Tsamis Prokopis Kontogiannis Ioannis Gourgiotis Stefanos Ntabos Ioannis Sarmas George Manis
author_facet	Konstantinos I. Tsamis Prokopis Kontogiannis Ioannis Gourgiotis Stefanos Ntabos Ioannis Sarmas George Manis
author_sort	Konstantinos I. Tsamis
title	Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning
title_short	Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning
title_full	Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning
title_fullStr	Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning
title_full_unstemmed	Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning
title_sort	automatic electrodiagnosis of carpal tunnel syndrome using machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8462da5c4d0546169eb70b8145bcc595
work_keys_str_mv	AT konstantinositsamis automaticelectrodiagnosisofcarpaltunnelsyndromeusingmachinelearning AT prokopiskontogiannis automaticelectrodiagnosisofcarpaltunnelsyndromeusingmachinelearning AT ioannisgourgiotis automaticelectrodiagnosisofcarpaltunnelsyndromeusingmachinelearning AT stefanosntabos automaticelectrodiagnosisofcarpaltunnelsyndromeusingmachinelearning AT ioannissarmas automaticelectrodiagnosisofcarpaltunnelsyndromeusingmachinelearning AT georgemanis automaticelectrodiagnosisofcarpaltunnelsyndromeusingmachinelearning
_version_	1718412966926221312

Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning

Ejemplares similares