"Perfusion Assessment of Healthy and Injured Hands Using Video-Based Deep Learning Models".

Journal: Plastic and reconstructive surgery
Published Date: May 28, 2025

Abstract

BACKGROUND: Assessing in-field hand trauma is challenging, and inaccurate perfusion assessment can substantially impact the patient and health system. Technology that enhances perfusion assessment could improve in-field triage. We present non-contact, video-based deep learning methods to classify perfused and ischemic fingers in control and acute trauma settings.

Authors

  • Vineet R Shenoy
    Department of Electrical and Computer Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD.
  • Carly Q Kingston
    The Curtis National Hand Center, MedStar Union Memorial Hospital, Baltimore, MD.
  • Mantej Singh
    Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD.
  • Ike C Fleming
    The Curtis National Hand Center, MedStar Union Memorial Hospital, Baltimore, MD.
  • Nicholas J Durr
  • Rama Chellappa
  • Aviram M Giladi
    The Curtis National Hand Center, MedStar Union Memorial Hospital, Baltimore, MD.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals