Journal of perinatology : official journal of the California Perinatal Association · 2025
Deep learning to assess laryngoscope insertion depth during neonatal intubation with video laryngoscopy.
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Majeedi Abrar, Peebles Patrick J, Li Yin, McAdams Ryan M
Аннотация
OBJECTIVE: Video laryngoscopy (VL) improves glottic visualization during neonatal intubation, but real-time guidance on blade insertion depth is lacking. We developed a deep learning model to classify insertion depth during VL as shallow, glottic zone, or deep.
STUDY DESIGN: A deep learning model was trained on 298,955 annotated frames from 132 neonatal VL videos from two NICUs on one platform with fivefold cross-validation. 31 clinicians were surveyed regarding preferred device feedback modalities.
RESULTS: The model detected glottic zone and shallow insertion depths with F1 scores of 0.894 and 0.718, respectively. Deep insertion events were rare (2.7%) with low performance (F1 = 0.034). Most clinicians preferred visual, minimal prompts over voice or haptic feedback.
CONCLUSION: AI-enabled VL may support blade-insertion depth assessment and training. Given the rarity of deep events, conclusions about deep insertion and clinical impact are limited. Future multi-site studies should focus on clinical integration and assessing outcomes.
STUDY DESIGN: A deep learning model was trained on 298,955 annotated frames from 132 neonatal VL videos from two NICUs on one platform with fivefold cross-validation. 31 clinicians were surveyed regarding preferred device feedback modalities.
RESULTS: The model detected glottic zone and shallow insertion depths with F1 scores of 0.894 and 0.718, respectively. Deep insertion events were rare (2.7%) with low performance (F1 = 0.034). Most clinicians preferred visual, minimal prompts over voice or haptic feedback.
CONCLUSION: AI-enabled VL may support blade-insertion depth assessment and training. Given the rarity of deep events, conclusions about deep insertion and clinical impact are limited. Future multi-site studies should focus on clinical integration and assessing outcomes.