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
摘要
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
徐晓龙,郏建波,张成霖,翁奇萍,邓水连,熊玉珍,黎妍文,秦传波,曾军英,刘长宇..机器学习辅助图像识别红细胞抗氧化研究[J].分析化学,2024,52(3):429-438,中插61-中插67,17.基金项目
国家自然科学基金项目(Nos.21974097,21675147)、广东省教育厅项目(Nos.2020KSYS004,2020ZDZX2015)、广东省科技创新战略专项资金(大学生科技创新培育)项目(No.pdjh2022b0531)、江门市科技局项目(No.2019030102360012639)和五邑大学大学生创新创业训练计划项目(No.202111349019)资助. Supported by the National Natural Science Foundation of China(Nos.21974097,21675147),the Educational Commission of Guangdong Province,China(Nos.2020KSYS004,2020ZDZX2015),the Special Funds for the Strategy of Guangdong College Students' Scientific and Technological Innovation,China(The Cultivation of College Students' Scientific and Technological Innovation,China)(No.pdjh2022b0531),the Jiangmen Municipal Science and Technology Bureau(No.2019030102360012639)and the Undergraduate Training Programs for Innovation and Entrepreneurship of Wuyi University,China(No.202111349019). （Nos.21974097,21675147）
