AI Article Synopsis
- Digital pathology allows for noninvasive measurement of pathological features and, when paired with artificial intelligence, it enhances information analysis and sharing, addressing knowledge gaps.
- Fibrosis, a key indicator of chronic inflammation, is crucial in assessing chronic liver diseases like viral hepatitis and metabolic dysfunction-related liver disease.
- This review explores how digital pathology can evaluate liver fibrosis prognosis, highlighting methods such as fiber quantification, collagen analysis, SHG/TPE microscopy, and Fibronest™.
Article Abstract
Digital pathology has enabled the noninvasive quantification of pathological parameters. In addition, the combination of digital pathology and artificial intelligence has enabled the analysis of a vast amount of information, leading to the sharing of much information and the elimination of knowledge gaps. Fibrosis, which reflects chronic inflammation, is the most important pathological parameter in chronic liver diseases, such as viral hepatitis and metabolic dysfunction-associated steatotic liver disease. It has been reported that the quantitative evaluation of various fibrotic parameters by digital pathology can predict the prognosis of liver disease and hepatocarcinogenesis. Liver fibrosis evaluation methods include 1 fiber quantification, 2 elastin and collagen quantification, 3 s harmonic generation/two photon excitation fluorescence (SHG/TPE) microscopy, and 4 Fibronest™. In this review, we provide an overview of role of digital pathology on the evaluation of fibrosis in liver disease and the characteristics of recent methods to assess liver fibrosis.
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| http://dx.doi.org/10.1007/s00795-024-00395-y | DOI Listing |
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