Multitask Deep Learning for Joint Detection of Necrotizing Viral and Noninfectious Retinitis From Common Blood and Serology Test Data.

Journal: Investigative ophthalmology & visual science
PMID: 38306107

Abstract

PURPOSE: Necrotizing viral retinitis is a serious eye infection that requires immediate treatment to prevent permanent vision loss. Uncertain clinical suspicion can result in delayed diagnosis, inappropriate administration of corticosteroids, or repeated intraocular sampling. To quickly and accurately distinguish between viral and noninfectious retinitis, we aimed to develop deep learning (DL) models solely using noninvasive blood test data.

Authors

  • Kai Tzu-Iunn Ong
    Department of Artificial Intelligence, Yonsei University College of Computing, Seoul, Republic of Korea.
  • Taeyoon Kwon
    Department of Artificial Intelligence, Yonsei University College of Computing, Seoul, Republic of Korea.
  • Harok Jang
    Department of Artificial Intelligence, Yonsei University College of Computing, Seoul, Republic of Korea.
  • Min Kim
    Department of Neurology, Ajou University School of Medicine, Suwon, Republic of Korea.
  • Christopher Seungkyu Lee
    Department of Ophthalmology, Institute of Vision Research, Severance Eye Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Suk Ho Byeon
    Department of Ophthalmology, Institute of Vision Research, Severance Eye Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Sung Soo Kim
    The Heart Center of Chonnam National University Hospital, 42 Jaebongro, Dong-gu, Gwangju 501-757, South Korea.
  • Jinyoung Yeo
    Department of Artificial Intelligence, Yonsei University College of Computing, Seoul, Republic of Korea.
  • Eun Young Choi
    Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.

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