Computer-vision based recognition of cervical spine stabilization during trauma resuscitation

Area of Science:

• Medical Imaging
• Computer Vision
• Trauma Care

Background:

• Cervical spine injuries pose significant risks, necessitating prompt stabilization during trauma resuscitation.
• Lapses in cervical spine stabilization are common, impacting patient outcomes.
• Automated monitoring systems are needed to evaluate and improve stabilization practices.

Purpose of the Study:

• To develop and evaluate a computer vision system for detecting cervical spine stabilization techniques.
• To enable scalable monitoring of stabilization timing and duration in trauma resuscitation.

Main Methods:

• A two-stage computer vision system was developed to identify patients and classify stabilization methods (rigid c-collar, semi-rigid c-collar, manual in-line stabilization).
• The system was trained and validated on 86 pediatric trauma resuscitation videos.
• Performance was assessed using accuracy, precision, recall, F1 score, and Matthews correlation coefficient (MCC).

Main Results:

• The system demonstrated high accuracy in detecting cervical spine stabilization techniques, with overall accuracy of 0.91.
• Detection accuracy for prehospital rigid c-collar (0.95), hospital semi-rigid c-collar (0.93), and manual in-line stabilization (0.97) was high.
• Manual in-line stabilization detection improved with the addition of simulation videos, indicating the benefit of balanced datasets.

Conclusions:

• The computer vision system shows excellent performance for detecting cervical spine stabilization.
• Limitations in detecting manual in-line stabilization were noted due to its rarity, but simulation data improved performance.
• This system can serve as a prototype for automated monitoring in trauma resuscitation settings.