Search Results for author:
Found 5 papers, 0 papers with code
Cognitive-Motor Integration in Assessing Bimanual Motor Skills
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.
One-shot domain adaptation in video-based assessment of surgical skills
This research marks the first instance of a domain-agnostic methodology for surgical skill assessment, paving the way for more precise and accessible training evaluation across diverse high-stakes environments such as real-life surgery where data is scarce.
Video-based Formative and Summative Assessment of Surgical Tasks using Deep Learning
To ensure satisfactory clinical outcomes, surgical skill assessment must be objective, time-efficient, and preferentially automated - none of which is currently achievable.
Deep Learning in fNIRS: A review
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.
Deep Neural Networks for the Assessment of Surgical Skills: A Systematic Review
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.
