Search Results for author:
Found 7 papers, 1 papers with code
Deep Learning for Video-Based Assessment of Endotracheal Intubation Skills
In the first phase, ETI is performed by two subject cohorts: Experts and Novices.
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 skill assessment in high-stakes domains with limited data via meta learning
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.
A deep learning model for burn depth classification using ultrasound imaging
The network first learns a low-dimensional manifold of the unburned skin images using an encoder-decoder architecture that reconstructs it from ultrasound images of burned skin.
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.
