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Updated: Jun 7, 2025
Minimally Invasive Murine Laryngoscopy for Close-Up Imaging of Laryngeal Motion During Breathing and Swallowing
Published on: December 1, 2023
Glottic opening detection using deep learning for 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:
This study aimed to develop an artificial intelligence (AI) method to augment video laryngoscopy (VL) by automating the detection of the glottic opening in neonates, as a step toward future studies on improving intubation outcomes.
Study Design:
A deep learning model, YOLOv8, was trained on 1623 video frames from 84 neonatal intubations to detect the glottic opening and evaluated using 14-fold cross-validation on metrics like precision and recall. Additionally, it was compared with 25 medical providers of varied intubation experience to assess its relative performance.
Results:
The model demonstrated a precision of 80.8% and a recall of 75.3% in identifying the glottic opening, detecting it 0.31 s faster than the average medical provider. It performed comparably or better than novice and intermediate providers, and slightly slower than experts.
Conclusion:
AI-powered tools can aid VL by providing real-time guidance, potentially enhancing neonatal intubation safety and efficiency for less experienced users.
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