Improving quality control in the routine practice for histopathological interpretation of gastrointestinal endoscopic biopsies using artificial intelligence.

Journal: PloS one
Published Date: Dec 15, 2022

Abstract

BACKGROUND: Colorectal and gastric cancer are major causes of cancer-related deaths. In Korea, gastrointestinal (GI) endoscopic biopsy specimens account for a high percentage of histopathologic examinations. Lack of a sufficient pathologist workforce can cause an increase in human errors, threatening patient safety. Therefore, we developed a digital pathology total solution combining artificial intelligence (AI) classifier models and pathology laboratory information system for GI endoscopic biopsy specimens to establish a post-analytic daily fast quality control (QC) system, which was applied in clinical practice for a 3-month trial run by four pathologists.

Authors

  • Young Sin Ko
    Pathology Center, Seegene Medical Foundation, Seoul, Republic of Korea.
  • Yoo Mi Choi
    Pathology Center, Seegene Medical Foundation, Seoul, Republic of Korea.
  • Mujin Kim
    Graduate School of Data Science, Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
  • Youngjin Park
    Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea. Electronic address: yodamaster@kaist.ac.kr.
  • Murtaza Ashraf
    Graduate School of Data Science, Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
  • Willmer Rafell Quiñones Robles
    Graduate School of Data Science, Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
  • Min-Ju Kim
    From the Department of Radiology and Research Institute of Radiology (J.C., H.J.H., J.B.S., S.M.L., K.J., R.P., J.K., N.K.), Department of Convergence Medicine, Biomedical Engineering Research Center (J. Yun), and Department of Clinical Epidemiology and Biostatistics (M.J.K.), University of Ulsan College of Medicine, Asan Medical Center, 86 Asanbyeongwon-Gil, Songpa-Gu, Seoul 138-735, Korea; Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea (J.J.); Department of Internal Medicine, Ajou University School of Medicine, Suwon, Korea (Y.L.); Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea (H.J.); and Coreline Soft, Seoul, Korea (J. Yi, D.Y., B.K.).
  • Jiwook Jang
    AI Research Team, Digital Innovation Sector, Seegene Medical Foundation, Seoul, Republic of Korea.
  • Seokju Yun
    AI Research Team, Digital Innovation Sector, Seegene Medical Foundation, Seoul, Republic of Korea.
  • Yuri Hwang
    AI Research Team, Digital Innovation Sector, Seegene Medical Foundation, Seoul, Republic of Korea.
  • Hani Jang
    AI Research Team, Digital Innovation Sector, Seegene Medical Foundation, Seoul, Republic of Korea.
  • Mun Yong Yi
    Graduate School of Data Science, Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.

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