Deep Learning for Video-Based Assessment of Endotracheal Intubation Skills

no code implementations • 17 Apr 2024 • Jean-Paul Ainam, Erim Yanik, Rahul Rahul, Taylor Kunkes, Lora Cavuoto, Brian Clemency, Kaori Tanaka, Matthew Hackett, Jack Norfleet, Suvranu De

In the first phase, ETI is performed by two subject cohorts: Experts and Novices.

One-shot skill assessment in high-stakes domains with limited data via meta learning

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.

Domain Adaptation Meta-Learning +1

A deep learning model for burn depth classification using ultrasound imaging

no code implementations • 29 Mar 2022 • Sangrock Lee, Rahul, James Lukan, Tatiana Boyko, Kateryna Zelenova, Basiel Makled, Conner Parsey, Jack Norfleet, Suvranu De

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.

Specificity

Deep Neural Networks for the Assessment of Surgical Skills: A Systematic Review

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.