AI Improves Vocal Cord Ultrasound Accuracy
Analysis
This paper demonstrates the potential of machine learning to improve the accuracy and reduce the operator-dependency of vocal cord ultrasound (VCUS) examinations. The high validation accuracies achieved by the segmentation and classification models suggest that AI can be a valuable tool for diagnosing vocal cord paralysis (VCP). This could lead to more reliable and accessible diagnoses.
Key Takeaways
- •Machine learning can automatically identify vocal cords in ultrasound images.
- •AI can distinguish between normal vocal cords and those affected by paralysis with high accuracy.
- •This technology has the potential to improve the accuracy and accessibility of vocal cord diagnoses.
Reference
“The best classification model (VIPRnet) achieved a validation accuracy of 99%.”