Application of Artificial Intelligence for Histopathological Diagnosis of Nonalcoholic Fatty Liver Disease/Nonalcoholic Steatohepatitis | Chapter 3 | Research Advances in Microbiology and Biotechnology Vol. 9

 This chapter outline the histopathology of nonalcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH), the basics of artificial intelligence (AI), and review recent progress concerning the application of AI in the pathological diagnosis of NAFLD/NASH. NAFLD/NASH is associated with metabolic syndrome and is rapidly increasing globally with a surge in the prevalence of obesity. Although non-invasive diagnosis of NAFLD/NASH has risen, the pathological evaluation of liver biopsy specimens remains the gold standard for diagnosing NAFLD/NASH. However, the pathological diagnosis of NAFLD/NASH relies on the subjective judgment of the pathologist, which results in non-negligible inter-observer variations. AI is an emerging tool in pathology that assists in diagnosis with high objectivity and accuracy. An increasing number of studies have reported the usefulness of AI in the pathological diagnosis of NAFLD/NASH, and our group has already used it in animal experiments. The use of AI in the pathological diagnosis of NAFLD/NASH is expected to advance from laboratory research to human clinical trials in the near future. AI offers great promise in overcoming the lack of objectivity and poor inter-observer agreement in the pathological diagnosis of NAFLD/NASH and warrants further research.

Author(s) Details:

Yoshihisa Takahashi,
Department of Pathology, School of Medicine, International University of Health and Welfare, 4-3 Kozunomori, Narita, Chiba 286-8686, Japan.

Erdenetsogt Dungubat,
Department of Pathology, School of Medicine, International University of Health and Welfare, 4-3 Kozunomori, Narita, Chiba 286-8686, Japan and Department of Pathology, School of Biomedicine, Mongolian National University of Medical Sciences, Jamyan St 3, Ulaanbaatar 14210, Mongolia.

Hiroyuki Kusano,
Department of Pathology, School of Medicine, International University of Health and Welfare, 4-3 Kozunomori, Narita, Chiba 286-8686, Japan.

Please see the link here: https://stm.bookpi.org/RAMB/article/view/12954