Division of Data Driven and Digital Medicine (D3 M).
Division of Nephrology, Department of Medicine.
Curr Opin Nephrol Hypertens. 2022 Jul 1;31(4):380-386. doi: 10.1097/MNH.0000000000000808. Epub 2022 Jun 10.
We seek to determine recent advances in kidney pathophysiology that have been enabled or enhanced by artificial intelligence. We describe some of the challenges in the field as well as future directions.
We first provide an overview of artificial intelligence terminologies and methodologies. We then describe the use of artificial intelligence in kidney diseases to discover risk factors from clinical data for disease progression, annotate whole slide imaging and decipher multiomics data. We delineate key examples of risk stratification and prognostication in acute kidney injury (AKI) and chronic kidney disease (CKD). We contextualize these applications in kidney disease oncology, one of the subfields to benefit demonstrably from artificial intelligence using all if these approaches. We conclude by elucidating technical challenges and ethical considerations and briefly considering future directions.
The integration of clinical data, patient derived data, histology and proteomics and genomics can enhance the work of clinicians in providing more accurate diagnoses and elevating understanding of disease progression. Implementation research needs to be performed to translate these algorithms to the clinical setting.
我们旨在确定人工智能在肾脏病理生理学方面的最新进展。我们描述了该领域的一些挑战以及未来的方向。
我们首先概述了人工智能的术语和方法。然后，我们描述了人工智能在肾脏疾病中的应用，包括从临床数据中发现疾病进展的风险因素、注释全切片成像和破译多组学数据。我们描述了急性肾损伤 (AKI) 和慢性肾脏病 (CKD) 中风险分层和预后的关键实例。我们将这些应用置于肾脏病肿瘤学中，这是一个明显受益于人工智能的子领域，所有这些方法都可以使用。最后，我们阐明了技术挑战和伦理考虑因素，并简要考虑了未来的方向。
将临床数据、患者衍生数据、组织学和蛋白质组学与基因组学相结合，可以增强临床医生提供更准确诊断和提高疾病进展理解的能力。需要进行实施研究，将这些算法转化到临床环境中。
