Deep learning to assess laryngoscope insertion depth during neonatal intubation with video laryngoscopy.

ABSTRACT:

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.

SUBMITTER: Majeedi A 

PROVIDER: S-EPMC12815674 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature