Development of an artificial intelligence-assisted system for tracheal intubation using a video laryngoscope in infants and neonates
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View abstract on PubMed
Summary
This summary is machine-generated.An artificial intelligence (AI) model aids in identifying the infant larynx during video laryngoscopy, improving intubation safety. However, further refinement is needed to address esophageal misidentification.
Area Of Science
- Medical Imaging
- Artificial Intelligence in Medicine
- Pediatric Anesthesiology
Background
- Laryngeal identification in infants via video laryngoscopy presents challenges.
- Accurate visualization is crucial for safe tracheal intubation.
Purpose Of The Study
- To develop an AI-assisted model for identifying the infant larynx, vocal cords, and arytenoid region.
- To enhance the safety of tracheal intubation in neonates and infants.
Main Methods
- Utilized 1197 images from 653 videos for training and validation.
- Developed an AI model using YOLOv8n architecture.
- Tested the model on 399 images from 63 videos.
Main Results
- AI model achieved 0.74 sensitivity, 0.99 specificity, and 0.91 AUC.
- Successfully identified vocal cords and arytenoid in unobstructed views.
- Observed esophageal misidentification and missed larynx detection due to obstacles.
Conclusions
- AI model effectively identifies key laryngeal structures during video laryngoscopy.
- Potential to improve safety of infant and neonatal tracheal intubation.
- Esophageal misidentification requires further investigation and model improvement.
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