no code implementations • 16 Apr 2024 • Erim Yanik, Xavier Intes, Suvranu De
Accurate assessment of bimanual motor skills is essential across various professions, yet, traditional methods often rely on subjective assessments or focus solely on motor actions, overlooking the integral role of cognitive processes.
1 code implementation • 16 Dec 2022 • Erim Yanik, Steven Schwaitzberg, Gene Yang, Xavier Intes, Jack Norfleet, Matthew Hackett, Suvranu De
This study marks the first instance of a domain-agnostic methodology for skill assessment in critical fields setting a precedent for the broad application of DL across diverse real-life domains with limited data.
no code implementations • 17 Mar 2022 • Erim Yanik, Uwe Kruger, Xavier Intes, Rahul Rahul, Suvranu De
To ensure satisfactory clinical outcomes, surgical skill assessment must be objective, time-efficient, and preferentially automated - none of which is currently achievable.
no code implementations • 31 Jan 2022 • Condell Eastmond, Aseem Subedi, Suvranu De, Xavier Intes
Results: Of the 63 papers considered in this review, 32 report a comparative study of deep learning techniques to traditional machine learning techniques where 26 have been shown outperforming the latter in terms of classification accuracy.
no code implementations • 3 Mar 2021 • Erim Yanik, Xavier Intes, Uwe Kruger, Pingkun Yan, David Miller, Brian Van Voorst, Basiel Makled, Jack Norfleet, Suvranu De
Here, we use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to systematically survey the literature on the use of Deep Neural Networks for automated and objective surgical skill assessment, with a focus on kinematic data as putative markers of surgical competency.