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

Cervical spine (c-spine) injuries can lead to significant disability and mortality. Although stabilization is the primary management for suspected c-spine injuries, lapses in stabilization frequently occur during trauma resuscitation. To facilitate evaluation of c-spine management, we developed a computer vision system to detect stabilization techniques. This system would enable scalable monitoring, including the timing and duration of c-spine stabilization.

We developed a 2-stage computer vision system to detect prehospital rigid c-collar, hospital semi-rigid c-collar, and manual in-line stabilization. The system was trained, tested, and validated using image frames extracted from 86 pediatric trauma resuscitation videos at a level 1 pediatric trauma center from October 2022 to May 2023. The first stage identified the patient in each image, and the second stage classified the stabilization technique. A 5-fold cross-validation was performed on the first 68 resuscitation videos for training/testing, with the latest 18 cases reserved for validation. System performance was evaluated using accuracy, precision, recall, F1 score, and Matthews correlation coefficient (MCC). To assess system potential for manual in-line detection, 10 simulation videos were added (eight for training, two for testing).

In the 18 validation cases, the system achieved high accuracy for binary classification (0.91) and for detecting specific stabilization techniques: prehospital rigid c-collar (0.95), hospital semi-rigid c-collar (0.93), and manual in-line stabilization (0.97). The precision scores were 0.89 for binary classification of any stabilization method, 0.71 for prehospital rigid c-collar, 0.89 for hospital semi-rigid c-collar, and 0.04 for manual in-line. Recall, F1, and MCC scores aligned with these findings, with the highest values observed for detecting the hospital semi-rigid c-collar among the stabilization techniques. Adding simulation videos improved manual in-line stabilization detection, with accuracy 0.62, precision 0.88, recall 0.58, F1 score 0.70, and MCC 0.27.

The 2-stage computer vision system showed excellent performance for detecting c-spine stabilization, with limitations for manual in-line stabilization due to its rarity. Simulation data improved manual in-line detection, highlighting potential benefits of a more balanced dataset. The computer vision system may serve as a prototype for automated monitoring of trauma resuscitation using the camera infrastructure in the resuscitation room.