Next Article in Journal / Special Issue
LICIC: Less Important Components for Imbalanced Multiclass Classification
Previous Article in Journal
Empirical Study on the Factors Influencing Process Innovation When Adopting Intelligent Robots at Small- and Medium-Sized Enterprises—The Role of Organizational Supports
Previous Article in Special Issue
Dynamic Handwriting Analysis for Supporting Earlier Parkinson’s Disease Diagnosis
Open AccessArticle

Towards Expert-Based Speed–Precision Control in Early Simulator Training for Novice Surgeons

CNRS UMR 7357 ICube Lab, Strasbourg University, Strasbourg 67000, France
Information 2018, 9(12), 316; https://doi.org/10.3390/info9120316
Received: 14 October 2018 / Revised: 1 December 2018 / Accepted: 5 December 2018 / Published: 9 December 2018
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
Simulator training for image-guided surgical interventions would benefit from intelligent systems that detect the evolution of task performance, and take control of individual speed–precision strategies by providing effective automatic performance feedback. At the earliest training stages, novices frequently focus on getting faster at the task. This may, as shown here, compromise the evolution of their precision scores, sometimes irreparably, if it is not controlled for as early as possible. Artificial intelligence could help make sure that a trainee reaches her/his optimal individual speed–accuracy trade-off by monitoring individual performance criteria, detecting critical trends at any given moment in time, and alerting the trainee as early as necessary when to slow down and focus on precision, or when to focus on getting faster. It is suggested that, for effective benchmarking, individual training statistics of novices are compared with the statistics of an expert surgeon. The speed–accuracy functions of novices trained in a large number of experimental sessions reveal differences in individual speed–precision strategies, and clarify why such strategies should be automatically detected and controlled for before further training on specific surgical task models, or clinical models, may be envisaged. How expert benchmark statistics may be exploited for automatic performance control is explained. View Full-Text
Keywords: surgical simulator training; individual performance trend; speed–accuracy function; automatic detection; performance feedback surgical simulator training; individual performance trend; speed–accuracy function; automatic detection; performance feedback
Show Figures

Graphical abstract

MDPI and ACS Style

Dresp-Langley, B. Towards Expert-Based Speed–Precision Control in Early Simulator Training for Novice Surgeons. Information 2018, 9, 316.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop