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Updated: Aug 21, 2026
Multiparametric Tumor Organoid Drug Screening Using Widefield Live-Cell Imaging for Bulk and Single-Organoid Analysis
Published on: December 23, 2022
The application and progress of AI-based image analysis in tumor organoid research
Yicheng Xu1, Shuai Fan1, Yuxin Chen1
1Beijing Key Laboratory of Gene Editing Therapy for Hereditary Neuromuscular Diseases, Key Laboratory of Molecular Medicine and Biological Diagnosis and Treatment (Ministry of Industry and Information Technology), Aerospace Center Hospital, Tangshan Research Institute, School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
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The development of preclinical models that recapitulate the physiological and pathological features of human tumors remains a central challenge in cancer research. Advances in cell biology have enabled the generation of three-dimensional tumor organoids, which closely mirror patient-specific therapeutic responses and facilitate the study of disease mechanisms. However, the trend of these models necessitates a shift from traditional, invasive analytical methods toward non-invasive, high-throughput imaging approaches. Here, we review the current state of tumor organoid culture and the emerging application of artificial intelligence (AI) in their evaluation. We discuss how AI-driven technologies are revolutionizing the analysis of fluorescence imaging, viability assessments, and dynamic cell tracking, thereby overcoming the limitations of manual interpretation. Finally, we provide a perspective on how integrating deep learning with organoid technology will enhance the precision and efficiency of drug discovery and personalized oncology.
