Abstract：
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.
摘要：
分裂细胞是由于对红细胞的机械损伤而产生的破碎的红细胞，通常是由于微血管病性血栓性疾病或机械因素。早期实验室检测裂孔细胞对及时诊断具有重要影响,有效治疗,以及血小板减少性紫癜和溶血性尿毒综合征等疾病的积极预后。由于科学技术的飞速发展,实验室血液学也取得了进展。由全自动化血液分析仪和全自动化形态学分析仪执行的测试的准确性和效率已经显著提高。近年来,计算能力和机器学习（ML）算法开发的实质性改进极大地扩展了自主机器潜力的极限。机器学习和人工智能（AI）的快速发展导致了分裂细胞自动检测的迭代和升级。然而,随着操作流程的显著简化，AI带来了挑战。本文综述了实验室血吸虫细胞检测的进展，血吸虫细胞与临床疾病的关系，和AI在血吸虫细胞检测中的进展。此外,讨论了当前的挑战和可能的解决方案，以及AI技术在外周血血吸虫细胞检测中的巨大潜力。
