Department of Nephrology, The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China; National Clinical Research Center for Child Health, Hangzhou, China.
Biomol Biomed. 2023 Mar 16;23(2):225-234. doi: 10.17305/bjbms.2022.8318.
Renal biopsy pathology is an essential gold standard for the diagnosis of most kidney diseases. With the increase in the incidence rate of kidney diseases, the lack of renal pathologists, and an imbalance in their distribution, there is an urgent need for a new renal pathological diagnosis model. Advances in artificial intelligence (AI) along with the growing digitization of pathology slides for diagnosis are promising approach to meet the demand for more accurate detection, classification, and prediction of the outcome of renal pathology. AI has contributed substantially to a variety of clinical applications, including renal pathology. Deep learning, a subfield of AI that is highly flexible and supports automatic feature extraction, is increasingly being used in multiple areas of pathology. In this narrative review, we first provide a general description of AI methods, and then discuss the current and prospective applications of AI in the field of renal pathology. Both diagnostic and predictive prognostic applications are covered, emphasizing AI in renal pathology images, predictive models, and 3D in renal pathology. Finally, we outline the challenges associated with the implementation of AI platforms in renal pathology and provide our perspective on how these platforms might change in this field.
肾活检病理学是诊断大多数肾脏疾病的重要金标准。随着肾脏疾病发病率的增加、肾病理学家的缺乏以及分布不均,迫切需要一种新的肾脏病理诊断模型。人工智能(AI)的进步以及用于诊断的病理学幻灯片的日益数字化,为满足对更准确检测、分类和预测肾脏病理结果的需求提供了有希望的方法。人工智能已在多种临床应用中做出了重大贡献,包括肾脏病理学。深度学习是人工智能的一个子领域,它具有高度的灵活性并支持自动特征提取,越来越多地应用于病理学的多个领域。在这篇叙述性综述中,我们首先提供了对 AI 方法的一般描述,然后讨论了 AI 在肾脏病理学领域的当前和潜在应用。涵盖了诊断和预测预后的应用,重点介绍了肾脏病理图像、预测模型和肾脏病理的 3D 中的 AI。最后,我们概述了在肾脏病理学中实施 AI 平台所面临的挑战,并就这些平台在该领域可能发生的变化提出了我们的看法。