Automation of surgical skill assessment using a three-stage machine learning algorithm

Abstract Surgical skills are associated with clinical outcomes. To improve surgical skills and thereby reduce adverse outcomes, continuous surgical training and feedback is required. Currently, assessment of surgical skills is a manual and time-consuming process which is prone to subjective interpre...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Joël L. Lavanchy, Joel Zindel, Kadir Kirtac, Isabell Twick, Enes Hosgor, Daniel Candinas, Guido Beldi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/dc51263bc4504396ad577f84e95bb043
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:dc51263bc4504396ad577f84e95bb043
record_format	dspace
spelling	oai:doaj.org-article:dc51263bc4504396ad577f84e95bb0432021-12-02T11:35:52ZAutomation of surgical skill assessment using a three-stage machine learning algorithm10.1038/s41598-021-84295-62045-2322https://doaj.org/article/dc51263bc4504396ad577f84e95bb0432021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84295-6https://doaj.org/toc/2045-2322Abstract Surgical skills are associated with clinical outcomes. To improve surgical skills and thereby reduce adverse outcomes, continuous surgical training and feedback is required. Currently, assessment of surgical skills is a manual and time-consuming process which is prone to subjective interpretation. This study aims to automate surgical skill assessment in laparoscopic cholecystectomy videos using machine learning algorithms. To address this, a three-stage machine learning method is proposed: first, a Convolutional Neural Network was trained to identify and localize surgical instruments. Second, motion features were extracted from the detected instrument localizations throughout time. Third, a linear regression model was trained based on the extracted motion features to predict surgical skills. This three-stage modeling approach achieved an accuracy of 87 ± 0.2% in distinguishing good versus poor surgical skill. While the technique cannot reliably quantify the degree of surgical skill yet it represents an important advance towards automation of surgical skill assessment.Joël L. LavanchyJoel ZindelKadir KirtacIsabell TwickEnes HosgorDaniel CandinasGuido BeldiNature 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 Joël L. Lavanchy Joel Zindel Kadir Kirtac Isabell Twick Enes Hosgor Daniel Candinas Guido Beldi Automation of surgical skill assessment using a three-stage machine learning algorithm
description	Abstract Surgical skills are associated with clinical outcomes. To improve surgical skills and thereby reduce adverse outcomes, continuous surgical training and feedback is required. Currently, assessment of surgical skills is a manual and time-consuming process which is prone to subjective interpretation. This study aims to automate surgical skill assessment in laparoscopic cholecystectomy videos using machine learning algorithms. To address this, a three-stage machine learning method is proposed: first, a Convolutional Neural Network was trained to identify and localize surgical instruments. Second, motion features were extracted from the detected instrument localizations throughout time. Third, a linear regression model was trained based on the extracted motion features to predict surgical skills. This three-stage modeling approach achieved an accuracy of 87 ± 0.2% in distinguishing good versus poor surgical skill. While the technique cannot reliably quantify the degree of surgical skill yet it represents an important advance towards automation of surgical skill assessment.
format	article
author	Joël L. Lavanchy Joel Zindel Kadir Kirtac Isabell Twick Enes Hosgor Daniel Candinas Guido Beldi
author_facet	Joël L. Lavanchy Joel Zindel Kadir Kirtac Isabell Twick Enes Hosgor Daniel Candinas Guido Beldi
author_sort	Joël L. Lavanchy
title	Automation of surgical skill assessment using a three-stage machine learning algorithm
title_short	Automation of surgical skill assessment using a three-stage machine learning algorithm
title_full	Automation of surgical skill assessment using a three-stage machine learning algorithm
title_fullStr	Automation of surgical skill assessment using a three-stage machine learning algorithm
title_full_unstemmed	Automation of surgical skill assessment using a three-stage machine learning algorithm
title_sort	automation of surgical skill assessment using a three-stage machine learning algorithm
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/dc51263bc4504396ad577f84e95bb043
work_keys_str_mv	AT joelllavanchy automationofsurgicalskillassessmentusingathreestagemachinelearningalgorithm AT joelzindel automationofsurgicalskillassessmentusingathreestagemachinelearningalgorithm AT kadirkirtac automationofsurgicalskillassessmentusingathreestagemachinelearningalgorithm AT isabelltwick automationofsurgicalskillassessmentusingathreestagemachinelearningalgorithm AT eneshosgor automationofsurgicalskillassessmentusingathreestagemachinelearningalgorithm AT danielcandinas automationofsurgicalskillassessmentusingathreestagemachinelearningalgorithm AT guidobeldi automationofsurgicalskillassessmentusingathreestagemachinelearningalgorithm
_version_	1718395851682873344

Automation of surgical skill assessment using a three-stage machine learning algorithm

Ejemplares similares