Artificial intelligence and computational pathology.
Artificial intelligence and computational pathology.
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DOI：
10.1038/s41374-020-00514-0
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发表时间：
2021-04
期刊：
影响因子：
--
通讯作者：
Zhang DY
中科院分区：
文献类型：
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作者：
Cui M;Zhang DY
数据处理和学习已成为医学进步的先锋，病理学和检验医学也不例外。通过临床信息学（包括基因组学、蛋白质组学、生物信息学和生物统计学）将科学研究融入临床实践，开启了患者护理的创新方法。计算病理学是病理学中新兴的亚专业，它为全幻灯片图像、多组学数据和临床信息学提供了更好的集成解决方案。然而，计算病理学面临着一些挑战，包括整合不同来源的原始数据的能力、硬件处理能力的限制、缺乏具体的培训计划，以及仍在固化的伦理问题和更大的社会可接受的实践问题。计算病理学整个行业的建立需要对连接患者和医生的三个基本要素进行深远的改变：本地实验室、扫描中心以及用于数据处理和检索的中央云中心/门户。通过信息集成和先进的数字通信网络解锁的计算病理学有可能提高临床工作流程效率、诊断质量，并最终为患者制定个性化的诊断和治疗计划。这篇综述描述了临床观点，并讨论了计算病理学的统计方法、临床应用、潜在障碍和未来方向。数据处理和学习已成为医学进步的先锋。计算病理学是一个新兴的亚专业，它有望为全切片图像、多组学数据和临床信息学提供更好的集成解决方案，作为患者护理的创新方法。这篇综述描述了临床观点，并讨论了计算病理学的统计方法、临床应用、潜在障碍和未来方向。
Data processing and learning has become a spearhead for the advancement of medicine, with pathology and laboratory medicine has no exception. The incorporation of scientific research through clinical informatics, including genomics, proteomics, bioinformatics, and biostatistics, into clinical practice unlocks innovative approaches for patient care. Computational pathology is burgeoning subspecialty in pathology that promises a better-integrated solution to whole-slide images, multi-omics data, and clinical informatics. However, computational pathology faces several challenges, including the ability to integrate raw data from different sources, limitation of hardware processing capacity, and a lack of specific training programs, as well as issues on ethics and larger societal acceptable practices that are still solidifying. The establishment of the entire industry of computational pathology requires far-reaching changes of the three essential elements connecting patients and doctors: the local laboratory, the scan center, and the central cloud hub/portal for data processing and retrieval. Computational pathology, unlocked through information integration and advanced digital communication networks, has the potential to improve clinical workflow efficiency, diagnostic quality, and ultimately create personalized diagnosis and treatment plans for patients. This review describes clinical perspectives and discusses the statistical methods, clinical applications, potential obstacles, and future directions of computational pathology. Data processing and learning has become a spearhead for the advancement of medicine. Computational pathology is burgeoning subspecialty that promises a better-integrated solution to whole-slide images, multi-omics data and clinical informatics as innovative approach for patient care. This review describes clinical perspectives and discusses the statistical methods, clinical applications, potential obstacles, and future directions of computational pathology.