Cheng Wangxinjun, Liu Jingshuang, Wang Chaofeng, Jiang Ruiyin, Jiang Mei, Kong Fancong
Center of Hematology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
Queen Mary College, Nanchang University, Nanchang, 330006, China.
Clin Exp Med. 2024 Aug 6;24(1):181. doi: 10.1007/s10238-024-01379-z.
Traditional manual blood smear diagnosis methods are time-consuming and prone to errors, often relying heavily on the experience of clinical laboratory analysts for accuracy. As breakthroughs in key technologies such as neural networks and deep learning continue to drive digital transformation in the medical field, image recognition technology is increasingly being leveraged to enhance existing medical processes. In recent years, advancements in computer technology have led to improved efficiency in the identification of blood cells in blood smears through the use of image recognition technology. This paper provides a comprehensive summary of the methods and steps involved in utilizing image recognition algorithms for diagnosing diseases in blood smears, with a focus on malaria and leukemia. Furthermore, it offers a forward-looking research direction for the development of a comprehensive blood cell pathological detection system.
传统的手工血涂片诊断方法既耗时又容易出错，其准确性往往严重依赖临床实验室分析人员的经验。随着神经网络和深度学习等关键技术的突破不断推动医疗领域的数字化转型，图像识别技术越来越多地被用于改进现有的医疗流程。近年来，计算机技术的进步使得通过使用图像识别技术提高了血涂片血细胞识别的效率。本文全面总结了利用图像识别算法诊断血涂片疾病（重点是疟疾和白血病）所涉及的方法和步骤。此外，还为全面的血细胞病理检测系统的开发提供了前瞻性的研究方向。
