Artificial Intelligence in Diagnostics: Enhancing Urine Test Accuracy Using a Mobile Phone-Based Reading System.

Journal: Annals of laboratory medicine
PMID: 39676422

Abstract

BACKGROUND: Urinalysis, an essential diagnostic tool, faces challenges in terms of standardization and accuracy. The use of artificial intelligence (AI) with mobile technology can potentially solve these challenges. Therefore, we investigated the effectiveness and accuracy of an AI-based program in automatically interpreting urine test strips using mobile phone cameras, an approach that may revolutionize point-of-care testing.

Authors

  • Hyun Jin Kim
    From the Department of Radiology and Research Institute of Radiology (M.K., H.S.K., H.J.K., J.E.P., S.J.K.), Department of Clinical Epidemiology and Biostatistics (S.Y.P.), and Department of Neurosurgery (Y.H.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-Gu, Seoul 05505, South Korea; GE Healthcare Korea, Seoul, Korea (J.L.); GE Healthcare Canada, Calgary, Canada (M.R.L.); and Department of Radiology, University of Calgary, Calgary, Canada (M.R.L.).
  • Manmyung Kim
    Robosapiens, Inc., Daejeon, Korea.
  • Hyunjae Zhang
    Robosapiens, Inc., Daejeon, Korea.
  • Hae Ri Kim
    Department of Dental Science, Graduate School, Kyungpook National University, Daegu 700-412, Korea. harry@knu.ac.kr.
  • Jae Wan Jeon
    Division of Nephrology, Department of Internal Medicine, Chungnam National University Sejong Hospital, Sejong, South Korea.
  • Yuri Seo
    Department of Family Medicine, Chungnam National University Sejong Hospital, Sejong, Korea.
  • Qute Choi
    Department of Laboratory Medicine, Chungnam National University Sejong Hospital, Chungnam National University School of Medicine, Daejeon, Korea.

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