Real-time medical phase recognition using long-term video understanding and progress gate method

Yanyi Zhang, Ivan Marsic, Randall S. Burd

Research output: Contribution to journalArticlepeer-review

Abstract

We introduce a real-time system for recognizing five phases of the trauma resuscitation process, the initial management of injured patients in the emergency department. We used depth videos as input to preserve the privacy of the patients and providers. The depth videos were recorded using a Kinect-v2 mounted on the sidewall of the room. Our dataset consisted of 183 depth videos of trauma resuscitations. The model was trained on 150 cases with more than 30 minutes each and tested on the remaining 33 cases. We introduced a reduced long-term operation (RLO) method for extracting features from long segments of video and combined it with the regular model having short-term information only. The model with RLO outperformed the regular short-term model by 5% using the accuracy score. We also introduced a progress gate (PG) method to distinguish visually similar phases using video progress. The final system achieved 91% accuracy and significantly outperformed previous systems for phase recognition in this setting.

Original languageEnglish (US)
Article number102224
JournalMedical Image Analysis
Volume74
DOIs
StatePublished - Dec 2021

All Science Journal Classification (ASJC) codes

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

Keywords

  • Deep learning
  • Phase recognition
  • Process gate
  • Reduced long-term operation
  • Trauma resuscitation
  • Video understanding

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