An AI-integrated organoid platform enables high-throughput functional evaluation of bioactive metal ions

Organoids have emerged as one of the most predictive preclinical models in medical research due to their ability to closely retain the genetic and phenotypic characteristics of original tissues. Nevertheless, the application in the systematic evaluation of innovative medical devices (especially biodegradable metals) remains largely unexplored, whereas conventional analytical approaches have significant limitations in data throughput, objectivity, and reproducibility. Recent advances in deep learning-based artificial intelligence (AI) image analysis offer powerful quantitative tools to overcome this bottleneck. In this study, we established a high-throughput quantitative in vitro evaluation platform for the dynamic assessment of biodegradable metal ions by integrating patient-derived colorectal cancer organoids with an OrganoSeg-based deep learning AI image analysis system. Systematic assessments revealed that Mg²⁺ and Zn²⁺ had significant concentration-dependent effects on organoid growth, and the organoid model exhibited sensitivity profiles distinct from conventional cell lines. RNA-seq analysis further revealed that high concentrations of Mg²⁺ induced cell cycle arrest by activating the p53/CDKN1C signaling axis. In contrast, high concentrations of Zn²⁺ disrupted intracellular zinc homeostasis by regulating metallothionein family members and the zinc transporter SLC39A10, triggering a robust inflammatory response and ultimately leading to apoptosis. This work not only confirms the considerable potential of integrating organoids with AI technology in the evaluation of medical devices, but also reveals the differential mechanism of action of bioactive metal ions Mg²⁺ and Zn²⁺ in a model closer to the human environment. This study establishes a reliable and generalizable paradigm for high-throughput and high-content biomedical research.