Institute of Pathology.
Washington University School of Medicine in St. Louis, Department of Pathology and Immunology, St. Louis, Missouri, USA.
Curr Opin Nephrol Hypertens. 2022 May 1;31(3):251-257. doi: 10.1097/MNH.0000000000000784. Epub 2022 Feb 14.
The field of pathology is currently undergoing a significant transformation from traditional glass slides to a digital format dependent on whole slide imaging. Transitioning from glass to digital has opened the field to development and application of image analysis technology, commonly deep learning methods (artificial intelligence [AI]) to assist pathologists with tissue examination. Nephropathology is poised to leverage this technology to improve precision, accuracy, and efficiency in clinical practice.
Through a multidisciplinary approach, nephropathologists, and computer scientists have made significant recent advances in developing AI technology to identify histological structures within whole slide images (segmentation), quantification of histologic structures, prediction of clinical outcomes, and classifying disease. Virtual staining of tissue and automation of electron microscopy imaging are emerging applications with particular significance for nephropathology.
AI applied to image analysis in nephropathology has potential to transform the field by improving diagnostic accuracy and reproducibility, efficiency, and prognostic power. Reimbursement, demonstration of clinical utility, and seamless workflow integration are essential to widespread adoption.
病理学领域目前正经历从传统玻璃切片向全玻片成像依赖的数字格式的重大转变。从玻璃到数字的转变使该领域能够开发和应用图像分析技术，通常是深度学习方法（人工智能[AI]）来协助病理学家进行组织检查。肾脏病学有望利用这项技术提高临床实践中的精确性、准确性和效率。
通过多学科方法，肾脏病学家和计算机科学家在开发 AI 技术以识别全玻片图像中的组织学结构（分割）、组织学结构的定量、临床结果的预测以及疾病分类方面取得了重大进展。组织的虚拟染色和电子显微镜成像的自动化是具有特别重要意义的新兴应用，对肾脏病学有重要意义。
应用于肾脏病学图像分析的人工智能有可能通过提高诊断准确性和可重复性、效率和预后能力来改变该领域。报销、临床实用性的证明以及无缝工作流程集成是广泛采用的关键。
