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Updated: Jan 14, 2026
A Modified Sonographic Algorithm for Image Acquisition in Life-Threatening Emergencies in the Critically Ill Newborn
Published on: April 7, 2023
Deep learning to assess laryngoscope insertion depth during neonatal intubation with video laryngoscopy
Abrar Majeedi1, Patrick J Peebles2, Yin Li1,3
1Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA.
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.
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