尽管癌症治疗最近取得了进展，但改进治疗药物仍然是肿瘤学家的一项关键任务。精确评估药物有效性需要使用 3D 细胞培养物而不是传统的 2D 单层细胞。微流控平台已经能够利用 3D 模型进行高通量药物筛选，但目前 3D 癌症球体的活力测定在可靠性和细胞毒性方面存在局限性。本研究引入了一种基于相衬图像的非破坏性、无标记活力估计的深度学习模型，为微流控中的连续球体监测提供了一种经济高效、高通量的解决方案。微流控技术促进了高通量癌症球体平台的创建，每个芯片大约有 12000 个球体，用于药物筛选。验证涉及八种传统化疗药物的测试，揭示了通过 LIVE/DEAD 染色评估的活力与相差形态学之间的强相关性。将模型的应用扩展到不在训练数据集中的新化合物和细胞系，产生了有希望的结果，这意味着通用活力估计模型的潜力。使用替代显微镜设置的实验支持该模型在不同实验室之间的可转移性。使用这种方法，我们还跟踪了给药过程中球体活力的动态变化。总之，这项研究将一个强大的平台与高通量微流控癌症球体测定和基于深度学习的活力估计相结合，广泛适用于各种细胞系、化合物和研究环境。

Despite recent advances in cancer treatment, refining therapeutic agents remains a critical task for oncologists. Precise evaluation of drug effectiveness necessitates the use of 3D cell culture instead of traditional 2D monolayers. Microfluidic platforms have enabled high-throughput drug screening with 3D models, but current viability assays for 3D cancer spheroids have limitations in reliability and cytotoxicity. This study introduces a deep learning model for non-destructive, label-free viability estimation based on phase-contrast images, providing a cost-effective, high-throughput solution for continuous spheroid monitoring in microfluidics. Microfluidic technology facilitated the creation of a high-throughput cancer spheroid platform with approximately 12 000 spheroids per chip for drug screening. Validation involved tests with eight conventional chemotherapeutic drugs, revealing a strong correlation between viability assessed via LIVE/DEAD staining and phase-contrast morphology. Extending the model's application to novel compounds and cell lines not in the training dataset yielded promising results, implying the potential for a universal viability estimation model. Experiments with an alternative microscopy setup supported the model's transferability across different laboratories. Using this method, we also tracked the dynamic changes in spheroid viability during the course of drug administration. In summary, this research integrates a robust platform with high-throughput microfluidic cancer spheroid assays and deep learning-based viability estimation, with broad applicability to various cell lines, compounds, and research settings.