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

Kang, Yeong Hun; Kim, Jin Youp; Kim, Young Jae; Kim, Sung Hyun; Kim, Kwang Gi; Rhee, Chae-Seo

doi:10.1007/s10278-024-01208-5

J Digit Imaging. Inform. med.

2024

DOI: 10.1007/s10278-024-01208-5

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

Yeong Hun Kang,

Jin Youp Kim,

Young Jae Kim

et al.

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Cited by 1 publication

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A Review of Application of Deep Learning in Endoscopic Image Processing

Nie,

Xu,

Wang

et al. 2024

J. Imaging

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Deep learning, particularly convolutional neural networks (CNNs), has revolutionized endoscopic image processing, significantly enhancing the efficiency and accuracy of disease diagnosis through its exceptional ability to extract features and classify complex patterns. This technology automates medical image analysis, alleviating the workload of physicians and enabling a more focused and personalized approach to patient care. However, despite these remarkable achievements, there are still opportunities to further optimize deep learning models for endoscopic image analysis, including addressing limitations such as the requirement for large annotated datasets and the challenge of achieving higher diagnostic precision, particularly for rare or subtle pathologies. This review comprehensively examines the profound impact of deep learning on endoscopic image processing, highlighting its current strengths and limitations. It also explores potential future directions for research and development, outlining strategies to overcome existing challenges and facilitate the integration of deep learning into clinical practice. Ultimately, the goal is to contribute to the ongoing advancement of medical imaging technologies, leading to more accurate, personalized, and optimized medical care for patients.

show abstract

A Review of Application of Deep Learning in Endoscopic Image Processing

Nie,

Xu,

Wang

et al. 2024

J. Imaging

View full text Add to dashboard Cite

show abstract

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

Cited by 1 publication

References 24 publications

A Review of Application of Deep Learning in Endoscopic Image Processing

A Review of Application of Deep Learning in Endoscopic Image Processing

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