Khalid Fizza, Alsadoun Lara, Khilji Faria, Mushtaq Maham, Eze-Odurukwe Anthony, Mushtaq Muhammad Muaz, Ali Husnain, Farman Rana Omer, Ali Syed Momin, Fatima Rida, Bokhari Syed Faqeer Hussain
Nephrology, Sharif Medical City Hospital, Lahore, PAK.
Trauma and Orthopedics, Chelsea and Westminster Hospital, London, GBR.
Cureus. 2024 May 12;16(5):e60145. doi: 10.7759/cureus.60145. eCollection 2024 May.
Chronic kidney disease (CKD) is a progressive condition characterized by gradual loss of kidney function, necessitating timely monitoring and interventions. This systematic review comprehensively evaluates the application of artificial intelligence (AI) and machine learning (ML) techniques for predicting CKD progression. A rigorous literature search identified 13 relevant studies employing diverse AI/ML algorithms, including logistic regression, support vector machines, random forests, neural networks, and deep learning approaches. These studies primarily aimed to predict CKD progression to end-stage renal disease (ESRD) or the need for renal replacement therapy, with some focusing on diabetic kidney disease progression, proteinuria, or estimated glomerular filtration rate (GFR) decline. The findings highlight the promising predictive performance of AI/ML models, with several achieving high accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve scores. Key factors contributing to enhanced prediction included incorporating longitudinal data, baseline characteristics, and specific biomarkers such as estimated GFR, proteinuria, serum albumin, and hemoglobin levels. Integration of these predictive models with electronic health records and clinical decision support systems offers opportunities for timely risk identification, early interventions, and personalized management strategies. While challenges related to data quality, bias, and ethical considerations exist, the reviewed studies underscore the potential of AI/ML techniques to facilitate early detection, risk stratification, and targeted interventions for CKD patients. Ongoing research, external validation, and careful implementation are crucial to leveraging these advanced analytical approaches in clinical practice, ultimately improving outcomes and reducing the burden of CKD.
慢性肾脏病（CKD）是一种进行性疾病，其特征是肾功能逐渐丧失，需要及时监测和干预。本系统评价全面评估了人工智能（AI）和机器学习（ML）技术在预测CKD进展中的应用。一项严格的文献检索确定了13项相关研究，这些研究采用了多种AI/ML算法，包括逻辑回归、支持向量机、随机森林、神经网络和深度学习方法。这些研究主要旨在预测CKD进展至终末期肾病（ESRD）或肾替代治疗的需求，一些研究关注糖尿病肾病进展、蛋白尿或估计肾小球滤过率（GFR）下降。研究结果突出了AI/ML模型具有前景的预测性能，其中一些模型在准确性、敏感性、特异性以及受试者工作特征曲线下面积得分方面表现出色。有助于提高预测的关键因素包括纳入纵向数据、基线特征以及特定生物标志物，如估计GFR、蛋白尿、血清白蛋白和血红蛋白水平。将这些预测模型与电子健康记录和临床决策支持系统相结合，为及时识别风险、早期干预和个性化管理策略提供了机会。尽管存在与数据质量、偏差和伦理考量相关的挑战，但所综述的研究强调了AI/ML技术在促进CKD患者早期检测、风险分层和靶向干预方面的潜力。持续的研究、外部验证和谨慎实施对于在临床实践中利用这些先进分析方法至关重要，最终改善治疗结果并减轻CKD负担。
