Hueso Miguel, Navarro Estanislao, Sandoval Diego, Cruzado Josep Maria
Nephrology Department, Hospital Universitari Bellvitge and Bellvitge Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain.
Independent Researcher, Barcelona, Spain.
Kidney Dis (Basel). 2019 Feb;5(1):3-10. doi: 10.1159/000492932. Epub 2018 Oct 10.
Renal transplantation is the treatment of choice for chronic kidney disease (CKD) patients, but the shortage of kidneys and the disabling medical conditions these patients suffer from make dialysis essential for most of them. Since dialysis drastically affects the patients' lifestyle, there are great expectations for the development of wearable artificial kidneys, although their use is currently impeded by major concerns about safety. On the other hand, dialysis patients with hemodynamic instability do not usually tolerate intermittent dialysis therapy because of their inability to adapt to a changing scenario of unforeseen events. Thus, the development of novel wearable dialysis devices and the improvement of clinical tolerance will need contributions from new branches of engineering such as artificial intelligence (AI) and machine learning (ML) for the real-time analysis of equipment alarms, dialysis parameters, and patient-related data with a real-time feedback response. These technologies are endowed with abilities normally associated with human intelligence such as learning, problem solving, human speech understanding, or planning and decision-making. Examples of common applications of AI are visual perception (computer vision), speech recognition, and language translation. In this review, we discuss recent progresses in the area of dialysis and challenges for the use of AI in the development of artificial kidneys.
Emerging technologies derived from AI, ML, electronics, and robotics will offer great opportunities for dialysis therapy, but much innovation is needed before we achieve a smart dialysis machine able to analyze and understand changes in patient homeostasis and to respond appropriately in real time. Great efforts are being made in the fields of tissue engineering and regenerative medicine to provide alternative cell-based approaches for the treatment of renal failure, including bioartificial renal systems and the implantation of bioengineered kidney constructs.
肾移植是慢性肾脏病（CKD）患者的首选治疗方法，但肾脏短缺以及这些患者所患的致残性疾病使得透析对大多数患者来说至关重要。由于透析会极大地影响患者的生活方式，人们对可穿戴人工肾的发展寄予厚望，尽管目前其使用因对安全性的重大担忧而受阻。另一方面，血流动力学不稳定的透析患者通常无法耐受间歇性透析治疗，因为他们无法适应不可预见事件不断变化的情况。因此，新型可穿戴透析设备的开发以及临床耐受性的提高将需要人工智能（AI）和机器学习（ML）等新工程分支的贡献，以便对设备警报、透析参数和患者相关数据进行实时分析并做出实时反馈响应。这些技术具有通常与人类智能相关的能力，如学习、解决问题、人类语音理解或规划与决策。AI的常见应用示例包括视觉感知（计算机视觉）、语音识别和语言翻译。在本综述中，我们讨论了透析领域的最新进展以及AI在人工肾开发中的应用挑战。
源自AI、ML、电子学和机器人技术的新兴技术将为透析治疗提供巨大机遇，但在我们实现一台能够分析和理解患者体内平衡变化并实时做出适当反应的智能透析机之前，仍需要大量创新。组织工程和再生医学领域正在做出巨大努力，以提供基于细胞的替代方法来治疗肾衰竭，包括生物人工肾系统和生物工程肾构建体的植入。
