โก Quick Summary
A recent study developed a computer vision system to enhance the recognition of cervical spine stabilization techniques during trauma resuscitation. The system demonstrated impressive accuracy, achieving a score of 0.91 in binary classification and high precision for specific stabilization methods.
๐ Key Details
- ๐ Dataset: 86 pediatric trauma resuscitation videos
- โ๏ธ Technology: 2-stage computer vision system
- ๐ Performance: Binary classification accuracy of 0.91
- ๐งฉ Techniques detected: Prehospital rigid c-collar, hospital semi-rigid c-collar, manual in-line stabilization
๐ Key Takeaways
- ๐ Cervical spine injuries can lead to significant disability and mortality.
- ๐ป The computer vision system can monitor c-spine stabilization techniques in real-time.
- ๐ High accuracy was achieved for detecting specific stabilization methods: 0.95 for prehospital rigid c-collar, 0.93 for hospital semi-rigid c-collar, and 0.97 for manual in-line stabilization.
- ๐ Simulation videos improved detection of manual in-line stabilization.
- ๐ Performance metrics included precision, recall, F1 score, and Matthews correlation coefficient (MCC).
- ๐ The system serves as a prototype for automated monitoring in trauma resuscitation settings.
๐ Background
Cervical spine (c-spine) injuries are a critical concern in trauma care, often resulting in severe consequences if not managed properly. Stabilization is essential for patients with suspected c-spine injuries, yet lapses in this process can occur during the chaotic environment of trauma resuscitation. The integration of technology, particularly computer vision, offers a promising avenue for improving the monitoring and management of these injuries.
๐๏ธ Study
Conducted at a level 1 pediatric trauma center from October 2022 to May 2023, this study aimed to develop a 2-stage computer vision system capable of detecting various c-spine stabilization techniques. The system was trained and validated using image frames from 86 pediatric trauma resuscitation videos, with a focus on identifying the patient and classifying the stabilization method employed.
๐ Results
The system achieved remarkable performance in the validation phase, with a binary classification accuracy of 0.91. Specific stabilization techniques were detected with high precision: prehospital rigid c-collar (0.95), hospital semi-rigid c-collar (0.93), and manual in-line stabilization (0.97). Although the detection of manual in-line stabilization was less robust, the inclusion of simulation videos enhanced its accuracy to 0.62.
๐ Impact and Implications
The findings from this study highlight the potential of computer vision technology in trauma care, particularly for monitoring c-spine stabilization. By automating the detection process, healthcare providers can ensure better compliance with stabilization protocols, ultimately improving patient outcomes. This technology could pave the way for more advanced monitoring systems in various medical settings, enhancing the quality of care delivered during critical moments.
๐ฎ Conclusion
This study underscores the significant advancements that computer vision can bring to trauma resuscitation practices. With high accuracy in detecting c-spine stabilization techniques, the proposed system represents a promising step towards automated monitoring in emergency care. Continued research and development in this area could lead to improved patient safety and outcomes in trauma situations.
๐ฌ Your comments
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Computer-vision based recognition of cervical spine stabilization during trauma resuscitation.
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.
Author: [‘Kim MS’, ‘Yuan S’, ‘Sippel GJ’, ‘Mun AH’, ‘Arkowitz DW’, ‘Marsic I’, ‘Burd RS’]
Journal: Injury
Citation: Kim MS, et al. Computer-vision based recognition of cervical spine stabilization during trauma resuscitation. Computer-vision based recognition of cervical spine stabilization during trauma resuscitation. 2025; 57:112951. doi: 10.1016/j.injury.2025.112951