光动力疗法（PDT）在肿瘤治疗中显示出前景，特别是与纳米技术相结合时。这项研究探讨了深度学习（尤其是 Cellpose 算法）对理解癌细胞对 PDT 反应的影响。 Cellpose 算法能够对癌细胞进行稳健的形态分析，而逻辑生长模型则可以预测 PDT 后的细胞行为。严格的模型验证确保了研究结果的准确性。 Cellpose 在 PDT 后表现出显着的形态变化，影响细胞增殖和存活。研究结果的可靠性通过模型验证得到证实。这种深度学习工具增强了我们对 PDT 后癌细胞动力学的理解。先进的分析技术，例如形态分析和生长建模，可以深入了解 PDT 对肝细胞癌细胞 (HCC) 的影响，从而有可能提高癌症治疗效果。总之，该研究探讨了深度学习在优化 PDT 参数以实现个性化肿瘤治疗和提高疗效方面的作用。

Photodynamic therapy (PDT) shows promise in tumor treatment, particularly when combined with nanotechnology. This study examines the impact of deep learning, particularly the Cellpose algorithm, on the comprehension of cancer cell responses to PDT. The Cellpose algorithm enables robust morphological analysis of cancer cells, while logistic growth modelling predicts cellular behavior post-PDT. Rigorous model validation ensures the accuracy of the findings. Cellpose demonstrates significant morphological changes after PDT, affecting cellular proliferation and survival. The reliability of the findings is confirmed by model validation. This deep learning tool enhances our understanding of cancer cell dynamics after PDT. Advanced analytical techniques, such as morphological analysis and growth modeling, provide insights into the effects of PDT on hepatocellular carcinoma (HCC) cells, which could potentially improve cancer treatment efficacy. In summary, the research examines the role of deep learning in optimizing PDT parameters to personalize oncology treatment and improve efficacy.