Development of an AI model for DILI-level prediction using liver organoid brightfield images

AI image processing techniques hold promise for clinical applications by enabling analysis of complex status information from cells. Importantly, real-time brightfield imaging has advantages of informativeness, non-destructive nature, and low cost over fluorescence imaging. Currently, human liver organoids (HLOs) offer an alternative to animal models due to their excellent physiological recapitulation including basic functions and drug metabolism. Here we show a drug-induced liver injury (DILI) level prediction model using HLO brightfield images (DILITracer) considering that DILI is the major causes of drug withdrawals. Specifically, we utilize BEiT-V2 model, pretrained on 700,000 cell images, to enhance 3D feature extraction. A total of 30 compounds from FDA DILIrank are selected (classified into Most-, Less-, and No-DILI) to activate HLOs and corresponding brightfield…