Airway and Airway Obstruction Site Segmentation Study Using U-Net with Drug-Induced Sleep Endoscopy Images.
Yeong Hun KangJin Youp KimYoung Jae KimSung Hyun KimYeoun Jae KimChae-Seo RheePublished in: Journal of imaging informatics in medicine (2024)
Obstructive sleep apnea is characterized by a decrease or cessation of breathing due to repetitive closure of the upper airway during sleep, leading to a decrease in blood oxygen saturation. In this study, employing a U-Net model, we utilized drug-induced sleep endoscopy images to segment the major causes of airway obstruction, including the epiglottis, oropharynx lateral walls, and tongue base. The evaluation metrics included sensitivity, specificity, accuracy, and Dice score, with airway sensitivity at 0.93 (± 0.06), specificity at 0.96 (± 0.01), accuracy at 0.95 (± 0.01), and Dice score at 0.84 (± 0.03), indicating overall high performance. The results indicate the potential for artificial intelligence (AI)-driven automatic interpretation of sleep disorder diagnosis, with implications for standardizing medical procedures and improving healthcare services. The study suggests that advancements in AI technology hold promise for enhancing diagnostic accuracy and treatment efficacy in sleep and respiratory disorders, fostering competitiveness in the medical AI market.
Keyphrases
- artificial intelligence
- drug induced
- deep learning
- liver injury
- healthcare
- big data
- machine learning
- sleep quality
- physical activity
- convolutional neural network
- obstructive sleep apnea
- primary care
- positive airway pressure
- minimally invasive
- small bowel
- combination therapy
- replacement therapy
- smoking cessation
- clinical evaluation