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. DILI is a leading cause of acute liver failure in the United States. Drug-related hepatotoxicity is uncommon, complex to diagnose, and can be life-threatening. Reported incidences are very less, one in 10,000. There is no diagnosis, if detected, no effective treatment, other than drug stopping and providing basic care. It is challenging to identify DILI affected cases. Thus, hepatotoxicity is the most common reason for post-marketing drug withdrawals. DILI is often nonspecific and misdiagnosed with other liver disease patterns, which affects patient care. To date, there is no scoring system available for DILI injury assessment.

The AutoML (Automated Machine Learning) model was tested to understand the injury patterns on a subset of 1,277 histology images for 10 drugs. The AutoML algorithm was able to classify necrotic injury patterns accurately with an average precision of 98.6% on a score threshold of 0.5. For this study, a subset of 10 common drugs associated with hepatic necrosis DILI phenotypes were selected which provides a compilation of hepatic injury drugs and toxins. The experiment design for DILI assessment was built around automated ML (AutoML) models. These models were built and tested as a proof of concept for DILI injury pattern classifications. The proposed models were evaluated on drug and dose-related injury classifications by analyzing the morphometry imaging features of liver histopathology whole slide images (WSI).

From a system perspective, a unique scoring system can be developed using machine learning ML techniques to define the etiology of DILI injury. AutoML is rapidly emerging as a tool for non-ML experts to implement a custom dataset. The present attempt is to develop a dose-dependent diagnostic support system for DILI injury classification. The use of feature-based AutoML approaches would be helpful for pharmaceutical companies, drug R&D labs, and research institutes in saving their time, money, and scientific efforts during clinical trials, to test drug efficacy and early detection of DILI injury.