AI Article Synopsis

  • The study aimed to evaluate how well emergency medical technicians can recognize agonal respiration under varying mobile network conditions while watching videos.
  • Five EMTs viewed 30 videos with different resolutions and frame rates, reporting the breathing patterns of patients and the time taken to identify them.
  • Results showed a high overall recognition accuracy of 80.7%, with frame rate impacting the speed of recognizing agonal respiration significantly more than video resolution.

Article Abstract

Aim: The mobile network quality in ambulances can be variable and limited. This pilot study aimed to identify a suitable network setting for recognizing agonal respiration under limited network conditions.

Methods: We recruited five emergency medical technicians, and each participant viewed 30 real-life videos with different resolutions, frame rates, and network scenarios. Thereafter, they reported the respiration pattern of the patient and identified agonal respiration cases. The time at which agonal respiration was identified was also recorded. The answers provided by the five participants were compared with those of two emergency physicians to compare the accuracy and time delay in breathing pattern recognition.

Results: The overall accuracy for initial respiratory pattern recognition was 80.7% (121/150). The accuracy for normal breathing was 93.3% (28/30), for not breathing was 96% (48/50), and for agonal breathing was 64.3% (45/70). There was no significant difference in successful recognition between video resolutions. However, the rate of time delay in recognizing agonal respiration less than 10 seconds between 15-fps group and 30-fps group had statistical significance (21% vs 52%,  = 0.041).

Conclusion: The frame rate emerges as one of critical factors in agonal respiration recognition through telemedicine, outweighing the significance of video resolution.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10320376PMC
http://dx.doi.org/10.1016/j.resplu.2023.100420DOI Listing

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