首页|机器学习辅助图像识别红细胞抗氧化研究

机器学习辅助图像识别红细胞抗氧化研究

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
细胞形态是原始的生物学特征,可以提供有关细胞生理或病理状况的内在信息.与通过肉眼比对分析细胞形态的方法相比,基于人工智能(Artificial intelligence,AI)的图像识别方法有望提高分析速度和精度.本研究建立了细胞形态图像分割、识别和计数的氧化损伤分析模型,并将其用于研究红细胞抗氧化的动态过程.结果表明,以正常红细胞比率计,本模型获得了与经典生化指标一致的结果,表明本模型可用于分析红细胞氧化损伤程度.本方法无需细胞染色或细胞破碎等操作,可在2h内获取结果;而且因为采用显微图像获取细胞形态信息,可实现细胞的实时监测.本模型有望拓展应用于环境毒理学有关细胞形态、细胞活性水平等方面的研究.
Study on Erythrocyte Antioxidantion Based on Machine Learning-assisted Image Recognition
Cell morphology,a pristine biological feature,provides intrinsic information on cell physiological or pathological conditions in a different manner than biochemical indicators.Image recognition methods based on artificial intelligence(AI)are helpful in analyzing speed and accuracy of cell morphology.In this study,an oxidative damage prediction model for cell morphology image segmentation,identification and counting was established,and the results were highly consistent with flow cytometric cell counts.Further,the established model was used to study the dynamic process of antioxidation in erythrocyte.The results showed that the ratio of normal morphological erythrocytes obtained by machine learning-assisted image recognition showed consistent trends with the classical biochemical indicators,which indicated that the model could effectively predict the degree of oxidative damage of red blood cells.Moreover,the method could also intuitively observe the real-time changes in erythrocyte morphology without staining or cell fragmentation.The model was expected to be expanded for rapid indicator screening,especially on cell morphology,cell viability and other environmental toxicology applications.

Image recognitionErythrocyte morphologyOxidative damageArtificial intelligence

徐晓龙、张成霖、翁奇萍、邓水连、熊玉珍、黎妍文、秦传波、曾军英、刘长宇、郏建波

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五邑大学环境与化学工程学院,江门 529020

五邑大学电子与信息工程学院,江门 529020

江门市人民医院,江门 529000

图像识别 红细胞形态 氧化损伤 人工智能

国家自然科学基金国家自然科学基金广东省教育厅项目广东省教育厅项目广东省科技创新战略专项资金(大学生科技创新培育)项目江门市科技局项目五邑大学大学生创新创业训练计划

21974097216751472020KSYS0042020ZDZX2015pdjh2022b05312019030102360012639202111349019

2024

10.19756/j.issn.0253-3820.231170

分析化学
中国化学会 中国科学院长春应用化学研究所

分析化学

CSTPCD北大核心
影响因子：1.423
ISSN：0253-3820
年,卷(期)：2024.52(3)
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