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 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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Publication Details

Journal
Communications Biology
Published
2025-06-07
DOI
https://doi.org/10.1038/s42003-025-08205-6
Citations
7
Primary Topic
Spectroscopy Techniques in Biomedical and Chemical Research
Type
article
Field-Weighted Citation Impact
11.44

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article

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

Tingting Xu, Yuepu Pu, Geyu Liang, Shiyi Tan et al.
7 citations
Communications Biology
Spectroscopy Techniques in Biomedical and Chemical Research
11.44
article

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

Tingting Xu, Yuepu Pu, Geyu Liang, Shiyi Tan, Zhongze Gu, Jianhua Rao, Zaozao Chen, Qinyi Hu, Ziliang Ye, Peng Xie, Xiaopeng Yan, Cheng Feng, Qiao Zhang, Juan Zhang, Xiaowei Wang, Mingyue Li, Wei Wang, Yan Ding, T. D. Hu
article en
7 citations

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.

Communications BiologyVol. 8(1)
Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Nanjing Sport Institute (CN), Southeast University (BD), State Key Laboratory of Digital Medical Engineering (CN), Southeast University (CN), Nanjing Medical University (CN)
Government of Jiangsu Province, Natural Science Foundation of Jiangsu Province, Nanjing Medical University
Good health and well-being
Openalex Percentile: Top 2%
Spectroscopy Techniques in Biomedical and Chemical Research
11.44
DOIOpen Access PDFOpenAlex
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