{"title":"人工智能时代的血吸虫检测。","authors":"Zeng Zhang, Su Yang, Xiuhong Wang","doi":"10.1111/ijlh.14260","DOIUrl":null,"url":null,"abstract":"<p>Schistocytes are fragmented red blood cells produced as a result of mechanical damage to erythrocytes, usually due to microangiopathic thrombotic diseases or mechanical factors. The early laboratory detection of schistocytes has a critical impact on the timely diagnosis, effective treatment, and positive prognosis of diseases such as thrombocytopenic purpura and hemolytic uremic syndrome. Due to the rapid development of science and technology, laboratory hematology has also advanced. The accuracy and efficiency of tests performed by fully automated hematology analyzers and fully automated morphology analyzers have been considerably improved. In recent years, substantial improvements in computing power and machine learning (ML) algorithm development have dramatically extended the limits of the potential of autonomous machines. The rapid development of machine learning and artificial intelligence (AI) has led to the iteration and upgrade of automated detection of schistocytes. However, along with significantly facilitated operation processes, AI has brought challenges. This review summarizes the progress in laboratory schistocyte detection, the relationship between schistocytes and clinical diseases, and the progress of AI in the detection of schistocytes. In addition, current challenges and possible solutions are discussed, as well as the great potential of AI techniques for schistocyte testing in peripheral blood.</p>","PeriodicalId":14120,"journal":{"name":"International Journal of Laboratory Hematology","volume":"46 3","pages":"427-433"},"PeriodicalIF":2.2000,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ijlh.14260","citationCount":"0","resultStr":"{\"title\":\"Schistocyte detection in artificial intelligence age\",\"authors\":\"Zeng Zhang, Su Yang, Xiuhong Wang\",\"doi\":\"10.1111/ijlh.14260\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Schistocytes are fragmented red blood cells produced as a result of mechanical damage to erythrocytes, usually due to microangiopathic thrombotic diseases or mechanical factors. The early laboratory detection of schistocytes has a critical impact on the timely diagnosis, effective treatment, and positive prognosis of diseases such as thrombocytopenic purpura and hemolytic uremic syndrome. Due to the rapid development of science and technology, laboratory hematology has also advanced. The accuracy and efficiency of tests performed by fully automated hematology analyzers and fully automated morphology analyzers have been considerably improved. In recent years, substantial improvements in computing power and machine learning (ML) algorithm development have dramatically extended the limits of the potential of autonomous machines. The rapid development of machine learning and artificial intelligence (AI) has led to the iteration and upgrade of automated detection of schistocytes. However, along with significantly facilitated operation processes, AI has brought challenges. This review summarizes the progress in laboratory schistocyte detection, the relationship between schistocytes and clinical diseases, and the progress of AI in the detection of schistocytes. In addition, current challenges and possible solutions are discussed, as well as the great potential of AI techniques for schistocyte testing in peripheral blood.</p>\",\"PeriodicalId\":14120,\"journal\":{\"name\":\"International Journal of Laboratory Hematology\",\"volume\":\"46 3\",\"pages\":\"427-433\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-03-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/ijlh.14260\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Laboratory Hematology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/ijlh.14260\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"HEMATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Laboratory Hematology","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/ijlh.14260","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"HEMATOLOGY","Score":null,"Total":0}
Schistocyte detection in artificial intelligence age
Schistocytes are fragmented red blood cells produced as a result of mechanical damage to erythrocytes, usually due to microangiopathic thrombotic diseases or mechanical factors. The early laboratory detection of schistocytes has a critical impact on the timely diagnosis, effective treatment, and positive prognosis of diseases such as thrombocytopenic purpura and hemolytic uremic syndrome. Due to the rapid development of science and technology, laboratory hematology has also advanced. The accuracy and efficiency of tests performed by fully automated hematology analyzers and fully automated morphology analyzers have been considerably improved. In recent years, substantial improvements in computing power and machine learning (ML) algorithm development have dramatically extended the limits of the potential of autonomous machines. The rapid development of machine learning and artificial intelligence (AI) has led to the iteration and upgrade of automated detection of schistocytes. However, along with significantly facilitated operation processes, AI has brought challenges. This review summarizes the progress in laboratory schistocyte detection, the relationship between schistocytes and clinical diseases, and the progress of AI in the detection of schistocytes. In addition, current challenges and possible solutions are discussed, as well as the great potential of AI techniques for schistocyte testing in peripheral blood.
期刊介绍:
The International Journal of Laboratory Hematology provides a forum for the communication of new developments, research topics and the practice of laboratory haematology.
The journal publishes invited reviews, full length original articles, and correspondence.
The International Journal of Laboratory Hematology is the official journal of the International Society for Laboratory Hematology, which addresses the following sub-disciplines: cellular analysis, flow cytometry, haemostasis and thrombosis, molecular diagnostics, haematology informatics, haemoglobinopathies, point of care testing, standards and guidelines.
The journal was launched in 2006 as the successor to Clinical and Laboratory Hematology, which was first published in 1979. An active and positive editorial policy ensures that work of a high scientific standard is reported, in order to bridge the gap between practical and academic aspects of laboratory haematology.