RT Journal Article
T1 Objective and automated assessment of surgical technical skills with IoT systems: A systematic literature review
A1 Castillo Segura, Pablo
A1 Fernández Panadero, María Carmen
A1 Alario-Hoyos, Carlos
A1 Muñoz Merino, Pedro José
A1 Delgado Kloos, Carlos
AB The 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.
PB Elsevier
SN 0933-3657
YR 2021
FD 2021-02
LK https://hdl.handle.net/10016/32081
UL https://hdl.handle.net/10016/32081
LA eng
NO This 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).
DS e-Archivo
RD 27 jul. 2024