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Airway and Airway Obstruction Site Segmentation Study Using U-Net with Drug-Induced Sleep Endoscopy Images.

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...

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Published in:Journal of Imaging Informatics in Medicine Vol. 38; no. 1; pp. 281 - 291
Main Authors: Kang, Yeong Hun, Kim, Jin Youp, Kim, Young Jae, Kim, Sung Hyun, Kim, Kwang Gi, Rhee, Chae-Seo
Format: pictorial research tables/charts Journal Article
Published: Springer Nature Feb2025
Online Access:View this record in EBSCOhost
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      dt: Feb2025
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10278-024-01208-5
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        atl: Airway and Airway Obstruction Site Segmentation Study Using U-Net with Drug-Induced Sleep Endoscopy Images.
      aug:
        au:
          Kang, Yeong Hun
          Kim, Jin Youp
          Kim, Young Jae
          Kim, Sung Hyun
          Kim, Kwang Gi
          Rhee, Chae-Seo
        affil: https://ror.org/03ryywt80 Dept. of Biomedical Engineering, College of Medicine, Gachon Univ, Incheon, Korea
      sug:
        subj:
          Airway Obstruction Diagnosis
          Endoscopy Methods
          Sleep Apnea, Obstructive Radiography
          Artificial Intelligence Utilization
          Human
          Data Analysis, Computer Assisted
          Software
          Sensitivity and Specificity
          Sleep Physiology
          Deep Learning Methods
          Funding Source
      ab: 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.
      pubtype: Academic Journal
      doctype:
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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