Development of an AI model for DILI-level prediction using liver organoid brightfield images
Abstract
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 images are collected at different time series and z-axis. Our computer vision model based on image-spatial-temporal coding layer excavates fully spatiotemporal information of continuously captured images, links HLO morphology with DILI severity, and final output DILI level of compounds. DILITracer achieves an overall accuracy of 82.34%. To our knowledge, this is the first model to output ternary classification of hepatotoxicity. Overall, DILITracer, using clinical data as an endpoint categorization label, offers a rapid and effective approach for screening hepatotoxic compounds.
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