Enhancing Histology Detection in MASH Cirrhosis for Artificial Intelligence Pathology Platform by Expert Pathologist Training

This study addresses the need for precise histopathological assessment of liver biopsies in Metabolic dysfunction-Associated Steatohepatitis (MASH) cirrhosis, where assessing nuanced drug effects on fibrosis becomes pivotal. The study describes a framework for the development and validation of an Artificial Intelligence (AI) model, leveraging SHG/TPE microscopy along with insights from an expert hepatopathologist, to precisely annotate fibrous septa and nodules in liver biopsies in MASH cirrhosis. A total of 25 liver biopsies from the Belapectin trial (NCT04365868) were randomly selected for training, and an additional 10 for validation. Each biopsy underwent three sections: Smooth Muscle Actin (SMA) and Sirius Red (SR) staining for septa and nodule annotation by pathologists and an unstained section for SHG/TPE imaging and AI annotation using qSepta and qNodule algorithms. Re-training of qSepta and qNodule algorithms was executed based on pathologist annotations. Sensitivity and positive predictive value (PPV) were employed to evaluate concordance with pathologist annotations, both pre- and post-training and during validation. Post-re-training by pathologist annotations, the AI demonstrated improved sensitivity for qSepta annotations, achieving 91% post-training (versus 84% pre-training). Sensitivity for qSepta in the validation cohort also improved to 91%. Additionally, PPV significantly improved from 69% pre-training to 85% post-training and reached 94% during validation. For qNodule annotations, sensitivity increased from 82% post-training to 90% in the validation cohort, while the PPV was consistent at 95% across both training and validation cohorts.This study outlines a strategic framework for developing and validating an AI model tailored for precise histopathological assessment of MASH cirrhosis, using pathologist training and annotations. The outcomes emphasise the crucial role of disease-specific customisation of AI models, based on expert pathologist training, in improving accuracy and applicability in clinical trials, marking a step forward in understanding and addressing the histopathological evaluation of MASH cirrhosis.