Deep Learning Analysis to Automatically Detect the Presence of Penetration or Aspiration in Videofluoroscopic Swallowing Study.

Journal: Journal of Korean medical science
PMID: 35166079

Abstract

BACKGROUND: Videofluoroscopic swallowing study (VFSS) is currently considered the gold standard to precisely diagnose and quantitatively investigate dysphagia. However, VFSS interpretation is complex and requires consideration of several factors. Therefore, considering the expected impact on dysphagia management, this study aimed to apply deep learning to detect the presence of penetration or aspiration in VFSS of patients with dysphagia automatically.

Authors

  • Jeoung Kun Kim
    Department of Business Administration, School of Business, Yeungnam University, Gyeongsan-si, Republic of Korea.
  • Yoo Jin Choo
    Department of Physical Medicine and Rehabilitation, College of Medicine, Yeoungnam University, 317-1, Daemyungdong, Namku, Daegu, 705-717, Republic of Korea.
  • Gyu Sang Choi
    Department of Information & Communication Engineering, Yeungnam University, Gyeongsan, Gyeongbuk, Korea.
  • Hyunkwang Shin
    Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, Republic of Korea.
  • Min Cheol Chang
    Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University, Taegu, Republic of Korea. Electronic address: wheel633@ynu.ac.kr.
  • Donghwi Park
    Department of Physical Medicine and Rehabilitation, Ulsan University Hospital, University of Ulsan College of Medicine, 877 Bangeojinsunghwndo-ro, Dong-gu, Ulsan, 44033, Republic of Korea. bdome@hanmail.net.

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