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Objective and automated assessment of surgical technical skills with IoT systems: A systematic literature review

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Aplicaciones y Servicios Telemáticos (GAST)es
dc.contributor.authorCastillo Segura, Pablo
dc.contributor.authorFernández Panadero, María Carmen
dc.contributor.authorAlario-Hoyos, Carlos
dc.contributor.authorMuñoz Merino, Pedro José
dc.contributor.authorDelgado Kloos, Carlos
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España)es
dc.contributor.funderComunidad de Madrides
dc.contributor.funderEuropean Commissionen
dc.date.accessioned2021-03-08T12:13:04Z
dc.date.available2022-02-05T00:00:05Z
dc.date.issued2021-02
dc.description.abstractThe assessment of surgical technical skills to be acquired by novice surgeons has been traditionally done by an expert surgeon and is therefore of a subjective nature. Nevertheless, the recent advances on IoT, the possibility of incorporating sensors into objects and environments in order to collect large amounts of data, and the progress on machine learning are facilitating a more objective and automated assessment of surgical technical skills. This paper presents a systematic literature review of papers published after 2013 discussing the objective and automated assessment of surgical technical skills. 101 out of an initial list of 537 papers were analyzed to identify: 1) the sensors used; 2) the data collected by these sensors and the relationship between these data, surgical technical skills and surgeons' levels of expertise; 3) the statistical methods and algorithms used to process these data; and 4) the feedback provided based on the outputs of these statistical methods and algorithms. Particularly, 1) mechanical and electromagnetic sensors are widely used for tool tracking, while inertial measurement units are widely used for body tracking; 2) path length, number of sub-movements, smoothness, fixation, saccade and total time are the main indicators obtained from raw data and serve to assess surgical technical skills such as economy, efficiency, hand tremor, or mind control, and distinguish between two or three levels of expertise (novice/intermediate/advanced surgeons); 3) SVM (Support Vector Machines) and Neural Networks are the preferred statistical methods and algorithms for processing the data collected, while new opportunities are opened up to combine various algorithms and use deep learning; and 4) feedback is provided by matching performance indicators and a lexicon of words and visualizations, although there is considerable room for research in the context of feedback and visualizations, taking, for example, ideas from learning analytics.en
dc.description.sponsorshipThis work was supported in part by the FEDER/Ministerio de Ciencia, Innovación y Universidades;Agencia Estatal de Investigación, through the Smartlet Project under Grant TIN2017-85179-C3-1-R, and in part by the Madrid Regional Government through the e-Madrid-CM Project under Grant S2018/TCS-4307, a project which is co-funded by the European Structural Funds (FSE and FEDER). Partial support has also been received from the European Commission through Erasmus + Capacity Building in the Field of Higher Education projects, more specifically through projects LALA (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), InnovaT (598758-EPP-1-2018-1-AT-EPPKA2-CBHE-JP), and PROF-XXI (609767-EPP-1-2019-1-ES-EPPKA2-CBHE-JP).en
dc.format.extent17
dc.identifier.bibliographicCitationCastillo-Segura, P., Fernández-Panadero, C., Alario-Hoyos, C., Muñoz-Merino, P. J., Delgado Kloos, C. (2021). Objective and automated assessment of surgical technical skills with IoT systems: A systematic literature review. Artificial Intelligence in Medicine, 112, 102007.en
dc.identifier.doihttps://doi.org/10.1016/j.artmed.2020.102007
dc.identifier.issn0933-3657
dc.identifier.publicationfirstpage1
dc.identifier.publicationlastpage17
dc.identifier.publicationtitleArtificial Intelligence in Medicineen
dc.identifier.publicationvolume112
dc.identifier.urihttps://hdl.handle.net/10016/32081
dc.identifier.uxxiAR/0000026539
dc.language.isoeng
dc.publisherElsevieren
dc.relation.projectIDGobierno de España. TIN2017-85179-C3-1-Res
dc.relation.projectIDComunidad de Madrid. S2018/TCS-4307es
dc.relation.projectIDComunidad de Madrid. S2018/TCS-4307es
dc.rights© 2020 Published by Elsevier.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherTechnical skillsen
dc.subject.otherSurgeryen
dc.subject.otherIoTen
dc.subject.otherSensorsen
dc.subject.otherStatistical methodsen
dc.subject.otherAlgorithmsen
dc.subject.otherLiterature reviewen
dc.titleObjective and automated assessment of surgical technical skills with IoT systems: A systematic literature reviewen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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