机器学习凭借其处理海量复杂数据的独特优势，为突破IgA肾病治疗及预后预测的现有瓶颈提供了新路径。在治疗决策方面，强化学习与多任务学习能够模拟不同临床干预方案的长期疗效，精准平衡治疗获益与潜在风险；在疗效评估方面，监督学习与图神经网络可有效筛选激素敏感型生物标志物，挖掘IgA肾病的关键分子通路；在预后预测方面，集成学习、DeepSurv等深度学习生存分析模型可实现IgA肾病的精细化风险分层，Transformer等时序模型则可动态追踪肾功能变化。本文综述了机器学习在IgA肾病精准治疗中的最新研究进展，及其在病理图像分析、移动健康平台等领域的应用现状，旨在推动IgA肾病由传统经验式治疗向数据驱动的精准治疗模式转变。

With its unique advantages in processing massive and complex data, machine learning has opened up new avenues to break through the existing bottlenecks in the treatment and prognostic prediction of IgA nephropathy. In terms of treatment decision-making, reinforcement learning and multi-task learning are capable of simulating the long-term efficacy of different clinical intervention plans, and accurately balance the therapeutic benefits and potential risks. In terms of efficacy evaluation, supervised learning and graph neural networks can effectively screen hormone-sensitive biomarkers and explore the key molecular pathways of IgA nephropathy. In terms of prognosis prediction, deep learning survival analysis models such as ensemble learning and DeepSurv can achieve refined risk stratification for IgA nephropathy, while temporal models including Transformer can dynamically track changes in renal function. This paper reviews the latest research progress of machine learning in the precise treatment of IgA nephropathy, as well as its application status in fields such as pathological image analysis and mobile health platforms, aiming to promote the transformation of IgA nephropathy treatment from traditional empirical therapy to a data-driven precision treatment mode.