机器学习在IgA肾病中应用的进展

Research progress of machine learning in IgA nephropathy

IgA肾病(IgAN)是世界范围内最常见的原发性肾小球疾病,临床及病理表现存在异质性,是导致终末期肾病的常见原因。早期发现并采取有效措施干预,对改善IgAN的结局尤为重要。机器学习方法能够使IgAN的病理分析、早期检测与诊断、预后预测等环节更加自动化和准确化。文章从病理诊断优化、无创性特异性生物标志物发现和疾病进展的预测及预后评估等方面对机器学习方法在IgAN中应用情况进行综述。

IgA nephropathy(IgAN)is the most common primary glomerulonephritis in the world with heterogeneous clinical and pathological manifestations.It is one of the leading causes of end-stage renal disease(ESRD).Early detection and effective intervention are especially important to improve the outcome of IgAN.Machine learning methods can make the pathological analysis,early detection and diagnosis,and prognosis prediction of IgAN more automated and accurate.The paper reviews application of machine learning methods in IgAN in pathological diagnosis optimization,non-invasive specific biomarker discovery,disease progression prediction and prognosis evaluation,and its development prospects.