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免疫性细胞死亡诱导剂的高通量选系统使用基于人工智能的实时图像分析.

Eunseo Kim¹, Donghoon Jang², Minji Kim¹

¹Department of Biomedical Science, Program in Biomedical Science and Engineering, Graduate school, Inha University, Incheon, 22212, Republic of Korea.

Computers in biology and medicine

|July 23, 2025

概括

一个人工智能 (AI) 系统通过分析细胞形态来选免疫细胞死亡 (ICD) 诱导物. 这种基于人工智能的方法可以有效地识别潜在的癌症免疫治疗剂,改善药物发现.

关键词:

细胞检测检测细胞检测与损伤相关的分子模式.高通量选的高通量选免疫性细胞死亡诱导剂实时图像分析实时图像分析转移学习转移学习深度学习是一种深度学习.

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科学领域:

免疫学免疫学免疫学
生物技术是生物技术.
计算生物学计算生物学

背景情况:

免疫性细胞死亡 (ICD) 对于将免疫学上"冷"的瘤转化为"热"的瘤至关重要,从而提高癌症免疫疗法的有效性.
由于需要快速大规模评估细胞形态和损伤相关分子模式 (DAMP) 动态,对ICD诱导物的有效查具有挑战性.
先进的图像处理能力对于开发高效的ICD选系统至关重要.

研究的目的:

开发一种基于人工智能 (AI) 的探测器,用于ICD诱导器的高通量选 (HTS).
使用AI识别与ICD相关的典型细胞形态.
为了提高ICD诱导选的效率和准确性.

主要方法:

开发了一种AI探测器,利用光标记器的转移学习和微调差异干扰对比 (DIC) 图像进行微调.
使用模型辅助标签 (MAL) 来提高注释效率并减少手动标签工作.
通过分析细胞死亡类型,DAMP释放和免疫激活来验证AI识别的候选人.

主要成果:

在一个盲测试中,AI系统成功地从八个候选者中识别了三种ICD诱导剂.
基于人工智能的HTS系统只使用实时光学图像高效地选ICD候选者,大大减少了时间和资源.
该系统展示了检测细微形态差异的能力,这些差异通常会被手动分析遗漏.

结论:

开发的基于人工智能的HTS系统提供了一种有效的方法来识别ICD诱导剂.
这种方法有可能加速新型癌症免疫疗法的发现.
人工智能系统对ICD预测,基础研究和药物发现中的更广泛的查应用具有前景.