AI-assisted, single-fiber digital pathology quantification of changes in liver fibrosis after bariatric surgery

Background: In steatohepatitis trials, antifibrotic effects are measured by conventional histology, albeit with significant limitations. We assessed how artificial intelligence–assisted, single-fiber quantitative image analysis of hepatic fibrosis matrix measures fibrosis reversal following an intervention that results in major histological improvement. Methods: Fifty-eight patients underwent bariatric surgery with preoperative and follow-up (>1 y) liver biopsies. High-resolution, single-fiber quantitative image analysis extracted quantitative traits of fibrosis, which were combined in a continuous phenotypic fibrosis composite score (Ph-FCS) of fibrosis severity. Changes in Ph-FCS were compared between patients with increasing, decreasing, or stable histological fibrosis stage. Results: The mean follow-up between biopsies was 5.84 years (SD 2.59). Forty patients improved in histological fibrosis stage, 12 were unchanged, and 6 progressed. Median Ph-FCS was reduced from 5.25 at baseline to 3.75 at follow-up ( p