Development of a Deep Learning-Based Epiglottis Obstruction Ratio Calculation System

Surgeons determine the treatment method for patients with epiglottis obstruction based on its severity, often by estimating the obstruction severity (using three obstruction degrees) from the examination of drug-induced sleep endoscopy images. However, the use of obstruction degrees is inadequate an...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-09, Vol.23 (18), p.7669
Main Authors: Su, Hsing-Hao, Lu, Chuan-Pin
Format: Article
Language:English
Subjects:
Airway management
Algorithms
Artificial intelligence
Cartilage
Computer vision
Deep learning
drug-induced sleep endoscopy
Endoscopy
Epiglottis
epiglottis obstruction
Esophagus
Imaging systems
Machine vision
Mouth
Neural networks
obstructive sleep apnea
Patients
region puzzle algorithm
Sleep
Sleep apnea
Surgeons
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Summary:Surgeons determine the treatment method for patients with epiglottis obstruction based on its severity, often by estimating the obstruction severity (using three obstruction degrees) from the examination of drug-induced sleep endoscopy images. However, the use of obstruction degrees is inadequate and fails to correspond to changes in respiratory airflow. Current artificial intelligence image technologies can effectively address this issue. To enhance the accuracy of epiglottis obstruction assessment and replace obstruction degrees with obstruction ratios, this study developed a computer vision system with a deep learning-based method for calculating epiglottis obstruction ratios. The system employs a convolutional neural network, the YOLOv4 model, for epiglottis cartilage localization, a color quantization method to transform pixels into regions, and a region puzzle algorithm to calculate the range of a patient’s epiglottis airway. This information is then utilized to compute the obstruction ratio of the patient’s epiglottis site. Additionally, this system integrates web-based and PC-based programming technologies to realize its functionalities. Through experimental validation, this system was found to autonomously calculate obstruction ratios with a precision of 0.1% (ranging from 0% to 100%). It presents epiglottis obstruction levels as continuous data, providing crucial diagnostic insight for surgeons to assess the severity of epiglottis obstruction in patients.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23187669