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Article Abstract
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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| http://dx.doi.org/10.1038/s41372-024-02171-3 | DOI Listing |
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