Data Showing Potential for Machine Learning to Advance Understanding of Nonalcoholic Steatohepatitis (NASH) Presented at the Liver Meeting 2019 BioSpace

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"By combining data from across our NASH clinical development program with artificial intelligence (AI)-based tools, we have the opportunity to better characterize this complex disease and understand how potential therapies can impact disease progression," said Mani Subramanian, MD, Senior Vice President, Liver Diseases, Gilead Sciences. "Applying PathAI's deep learning research platform for liver histology assessment will enable a more rigorous review of treatment response and has potential for the exploration of novel biology in patients with advanced fibrosis due to NASH." In a collaboration with PathAI, a leader in AI-powered research in pathology, Gilead is evaluating machine learning approaches to liver histology assessment for use in the diagnosis and staging of NASH and monitoring of treatment response in clinical trials. A study of images from liver biopsies from patients screened for the Phase 3 STELLAR program compared the staging and characterization of liver disease as assessed by experienced pathologists and by the PathAI research platform. The pathologists scored biopsies using the NASH Clinical Research Network (CRN) and Ishak fibrosis classifications, and the PathAI research platform, a convolutional neural network, evaluated these biopsies following training on more than 68,000 annotations from 75 board-certified pathologists.