Improving the Computer-Aided Estimation of Ulcerative Colitis Severity According to Mayo Endoscopic Score by Using Regression-Based Deep Learning.

Journal: Inflammatory bowel diseases
PMID: 36382800

Abstract

BACKGROUND: Assessment of endoscopic activity in ulcerative colitis (UC) is important for treatment decisions and monitoring disease progress. However, substantial inter- and intraobserver variability in grading impairs the assessment. Our aim was to develop a computer-aided diagnosis system using deep learning to reduce subjectivity and improve the reliability of the assessment.

Authors

  • Gorkem Polat
    Graduate School of Informatics, Middle East Technical University, Ankara, Turkey.
  • Haluk Tarik Kani
    Department of Gastroenterology, School of Medicine, Marmara University, Istanbul, Turkey.
  • Ilkay Ergenc
    Department of Gastroenterology, School of Medicine, Marmara University, Istanbul, Turkey.
  • Yesim Ozen Alahdab
    Department of Gastroenterology, School of Medicine, Marmara University, Istanbul, Turkey.
  • Alptekin Temizel
    Graduate School of Informatics, Middle East Technical University, Ankara, Turkey.
  • Ozlen Atug
    Department of Gastroenterology, School of Medicine, Marmara University, Istanbul, Turkey.

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