Abstract
BACKGROUND: 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. METHODS: 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). RESULTS: 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. CONCLUSION: 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.