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Deep Learning Analysis to Automatically Detect the Presence of Penetration or Aspiration in Videofluoroscopic Swallowing Study.
Kim, Jeoung Kun; Choo, Yoo Jin; Choi, Gyu Sang; Shin, Hyunkwang; Chang, Min Cheol; Park, Donghwi.
Affiliation
  • Kim JK; Department of Business Administration, School of Business, Yeungnam University, Gyeongsan, Korea.
  • Choo YJ; Department of Rehabilitation Medicine, College of Medicine, Yeungnam University, Daegu, Korea.
  • Choi GS; Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, Korea.
  • Shin H; Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, Korea.
  • Chang MC; Department of Rehabilitation Medicine, College of Medicine, Yeungnam University, Daegu, Korea. wheel633@gmail.com.
  • Park D; Department of Physical Medicine and Rehabilitation, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Korea. bdome@hanmail.net.
J Korean Med Sci ; 37(6): e42, 2022 Feb 14.
Article in En | MEDLINE | ID: mdl-35166079
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Video Recording / Fluoroscopy / Deglutition Disorders / Deglutition / Deep Learning Type of study: Prognostic_studies Limits: Aged / Aged80 / Female / Humans / Male / Middle aged Language: En Journal: J Korean Med Sci Journal subject: MEDICINA Year: 2022 Type: Article
Fulltext
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PubMed Links
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Video Recording / Fluoroscopy / Deglutition Disorders / Deglutition / Deep Learning Type of study: Prognostic_studies Limits: Aged / Aged80 / Female / Humans / Male / Middle aged Language: En Journal: J Korean Med Sci Journal subject: MEDICINA Year: 2022 Type: Article