PathAI and Novo Nordisk present AI-powered Scoring Showing Significant Reduction of Fibrosis Following Semaglutide Treatment in Patients With NASH

PathAI, a global leader of AI-powered technology applied to pathology, announced new data showing AI-based assessment, including use of PathAI’s AIM-NASH tool, of key histological features of non-alcoholic steatohepatitis (NASH) following semaglutide treatment. These results will be shared in the poster presentation, “Artificial intelligence-powered digital pathology model supports that fibrosis is reduced by semaglutide in patients with NASH,” at The AASLD Liver Meeting 2021.

In a joint research collaboration, Novo Nordisk A/S and PathAI compared manual and AI-based evaluation of liver biopsy samples from a phase 2 trial that measured the effect of different dosages of the glucagon-like peptide-1 receptor agonist semaglutide in patients with NASH. PathAI machine learning models were deployed to identify and quantify NASH histologic features and generate NASH Clinical Research Network (CRN) NAFLD Activity Scores and CRN fibrosis stages.

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The results from this study indicated that via both manual and AI assessment, NASH resolution was dose dependent, with a higher proportion of patients receiving semaglutide 0.4mg achieving NASH resolution without fibrosis worsening, compared to patients receiving placebo.

AI assessment using categorical scoring showed a moderately higher proportion of patients achieving improvement in fibrosis stage with semaglutide 0.4mg compared with placebo.  Notably, AI assessment using continuous scoring algorithms showed a statistically significant reduction in fibrosis with semaglutide 0.4 mg vs. placebo (p=0.0099).

AI- and pathologist-based assessments align in demonstrating a significant effect of semaglutide in reducing NASH disease severity as assessed by histology.  Of particular note, this study shows that quantitative evaluation enabled by AI-based continuous scoring reveals significant reduction in fibrosis upon semaglutide treatment, highlighting the capability of AI-based assessment to provide additional clinically relevant insights into histological changes resulting from treatment.

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