![[Translate to chinese:] Image of confluent cells taken with phase contrast (left) and analyzed for confluency using AI (right). Confluent_cells_with_phase_contrast_and_analyzed_for_confluency_using_AI.jpg](/fileadmin/_processed_/3/6/csm_Confluent_cells_with_phase_contrast_and_analyzed_for_confluency_using_AI_b3bba2d107.jpg "[Translate to chinese:] Image of confluent cells taken with phase contrast (left) and analyzed for confluency using AI (right).")
传统与AI汇合度评估方法
通过人工细胞培养检查和图像分析进行汇合度评估存在显著局限性。这种劣势在处理细胞形态复杂、细胞拥挤以及动态实验条件的情况下尤为明显。人工评估容易受到主观性误差的影响，因此存在不一致结果的严重风险。
这种显著局限性可能影响汇合度测量的精度和可靠性。因此，需要采用 AI 技术，如自动图像分析和深度学习，提供先进解决方案，使汇合度评估变得高效且可重复，尤其是在处理动态和多样化的细胞环境时。
AI汇合度测量的优势
基于AI的汇合度评估提供以下优势：
- 由于先进模型如卷积神经网络（CNN），能够适应多样化的细胞形态
- 在处理复杂和多样化细胞系时具有实验灵活性
- 对变化的实验条件动态响应，使研究人员能够更细致地了解动态细胞环境中的汇合度
- 用于复杂和拥挤培养物的边缘检测，使细胞边界能够清晰识别
- 优于手动方法的稳健可靠分析
![[Translate to chinese:] Mouse brain (left) microdissected with a 10x objective (upper right). Inspection of the collection device (lower right).](/fileadmin/_processed_/f/3/csm_Mouse_brain_microdissected_with_10x_objective_d8dd838d2c.jpg "[Translate to chinese:] Mouse brain (left) microdissected with a 10x objective (upper right). Inspection of the collection device (lower right).")
![[Translate to chinese:] AI-based cell counting performed with a phase-contrast and fluorescence image using the Mateo FL microscope.](/fileadmin/_processed_/d/9/csm_AI-based_cell_counting_performed_with_phase-contrast_and_fluorescence_image_6cf61db52d.jpg "[Translate to chinese:] AI-based cell counting performed with a phase-contrast and fluorescence image using the Mateo FL microscope.")
