Automated Machine Learning Diagnostic Support System as a Computational Biomarker for Detecting Drug-Induced Liver Injury Patterns in Whole Slide Liver Pathology Images.

ABSTRACT: Drug-induced liver injury (DILI) is a challenging disease to diagnose, a leading cause of acute liver failure, and responsible for drug withdrawal from the market. There is no symptom, no biomarker or test for detection, no therapy, but discontinuation of the drug. Pharmaceutical companies spend huge money, time, and scientific research efforts to test DILI effects and drug efficacy. A preclinical diagnostic support system is designed and proposed for DILI detection and classification on liver biopsy histopathology images. Heterogeneity features and automated machine learning (AutoML) models were tested to classify DILI injury patterns on whole slide image. Fractal and lacunarity values were used to detect hepatocellular necrotic injury patterns caused on a rat liver (in vivo) by

SUBMITTER: Puri M 

PROVIDER: S-EPMC6998050 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature