AI Article Synopsis

  • The study investigates the use of second harmonic generation (SHG) microscopy combined with artificial intelligence to analyze liver fibrosis in children with autoimmune liver disease (AILD) and aims to find prognostic biomarkers for these patients.
  • Researchers analyzed liver biopsy slides from 63 patients to evaluate fibrosis patterns and their correlation with clinical outcomes, and found a significant association between certain fibrosis characteristics and increased risk of liver complications and transplantation.
  • The findings suggest that SHG and AI can enhance understanding of liver histopathology in AILD, potentially guiding better management and treatment decisions for affected children.

Article Abstract

Background: Children with autoimmune liver disease (AILD) may develop fibrosis-related complications necessitating a liver transplant. We hypothesize that tissue-based analysis of liver fibrosis by second harmonic generation (SHG) microscopy with artificial intelligence analysis can yield prognostic biomarkers in AILD.

Methods: Patients from single-center studies with unstained slides from clinically obtained liver biopsies at AILD diagnosis were identified. Baseline demographics and liver biochemistries at diagnosis and 1 year were collected. Clinical endpoints studied included the presence of varices, variceal bleeding, ascites, HE, and liver transplant. In collaboration with HistoIndex, unstained slides underwent SHG/artificial intelligence analysis to map fibrosis according to 10 quantitative fibrosis parameters based on tissue location, including total, periportal, perisinusoidal, and pericentral area and length of strings.

Results: Sixty-three patients with AIH (51%), primary sclerosing cholangitis (30%), or autoimmune sclerosing cholangitis (19%) at a median of 14 years old (range: 3-24) were included. An unsupervised analysis of quantitative fibrosis parameters representing total and portal fibrosis identified a patient cluster with more primary sclerosing cholangitis/autoimmune sclerosing cholangitis. This group had more fibrosis at diagnosis by METAVIR classification of histopathological review of biopsies (2.5 vs. 2; p = 0.006). This quantitative fibrosis pattern also predicted abnormal 12-month ALT with an OR of 3.6 (1.3-10, p = 0.014), liver complications with an HR of 3.2 (1.3-7.9, p = 0.01), and liver transplantation with an HR of 20.1 (3-135.7, p = 0.002).

Conclusions: The application of SHG/artificial intelligence algorithms in pediatric-onset AILD provides improved insight into liver histopathology through fibrosis mapping. SHG allows objective identification of patients with biliary tract involvement, which may be associated with a higher risk for refractory disease.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11637754PMC
http://dx.doi.org/10.1097/HC9.0000000000000594DOI Listing

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