机器学习辅助图像识别红细胞抗氧化研究
Study on Erythrocyte Antioxidantion Based on Machine Learning-assisted Image Recognition
徐晓龙 1张成霖 1翁奇萍 2邓水连 3熊玉珍 3黎妍文 1秦传波 2曾军英 2刘长宇 1郏建波1
作者信息
- 1. 五邑大学环境与化学工程学院,江门 529020
- 2. 五邑大学电子与信息工程学院,江门 529020
- 3. 江门市人民医院,江门 529000
- 折叠
摘要
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.
Abstract
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.
关键词
图像识别/红细胞形态/氧化损伤/人工智能Key words
Image recognition/Erythrocyte morphology/Oxidative damage/Artificial intelligence引用本文复制引用
基金项目
国家自然科学基金(21974097)
国家自然科学基金(21675147)
广东省教育厅项目(2020KSYS004)
广东省教育厅项目(2020ZDZX2015)
广东省科技创新战略专项资金(大学生科技创新培育)项目(pdjh2022b0531)
江门市科技局项目(2019030102360012639)
五邑大学大学生创新创业训练计划(202111349019)
出版年
2024
国家科技期刊平台
NSTL
万方数据