AI Article Synopsis
- The study investigates the use of Artificial Intelligence (AI) to assess liver fibrosis in patients with Metabolic-Associated Steatotic Hepatitis (MASH) more accurately than traditional methods.
- Out of 60 patients, biopsies were analyzed using AI technology to measure features like collagen area and entropy, revealing significant differences across fibrosis stages and treatment responses.
- Results showed that AI could identify changes in fibrosis in 76% of cases post-treatment, suggesting it offers a more reliable way to evaluate disease progression and treatment efficacy.
Article Abstract
Background And Aims: The risk of disease progression in MASH increases proportionally to the pathological stage of fibrosis. This latter is evaluated through a semi-quantitative process, which has limited sensitivity in reflecting changes in disease or response to treatment. This study aims to test the clinical impact of Artificial Intelligence (AI) in characterizing liver fibrosis in MASH patients.
Methods: The study included 60 patients with clinical pathological diagnosis of MASH. Among these, 17 received a medical treatment and underwent a post-treatment biopsy. For each biopsy (n = 77) a Sirius Red digital slide (SR-WSI) was obtained. AI extracts >30 features from SR-WSI, including estimated collagen area (ECA) and entropy of collagen (EnC).
Results: AI highlighted that different histopathological stages are associated with progressive and significant increase of ECA (F2: 2.6% ± 0.4; F3: 5.7% ± 0.4; F4: 10.9% ± 0.8; p: 0.0001) and EnC (F2: 0.96 ± 0.05; F3: 1.24 ± 0.06; F4: 1.80 ± 0.11, p: 0.0001); disclosed the heterogeneity of fibrosis among pathological homogenous cases; revealed post treatment fibrosis modification in 76% of the cases ( 56% detected by histopathology).
Conclusion: AI characterizes the fibrosis process by its true, continuous, and non-categorical nature, thus allowing for better identification of the response to anti-MASH treatment.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11532183 | PMC |
| http://dx.doi.org/10.3389/fmed.2024.1480866 | DOI Listing |
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