AI 中文总结
本研究系统综述2000-2025年239篇AI应用于DBS治疗运动障碍的文献，发现多数系统处于早期转化阶段，验证不足是主要限制，新兴研究显示领域向临床成熟发展。
AI 中文摘要
人工智能（AI）正被越来越多地探索应用于运动障碍的深部脑刺激（DBS）领域，但目前系统是否接近部署仍不明确。为明确其范围、验证成熟度和转化就绪度，我们系统评估了2000年至2025年间发表的239篇同行评审研究，评估了AI方法、验证实践和限制临床转化的障碍。研究主要集中于帕金森病和丘脑下核靶向，对其他障碍和靶点的覆盖有限。多数研究报告了令人鼓舞的内部性能，但外部验证罕见，评估仍以回顾性和单中心为主，超过四分之一的研究涉及小样本、高维数据集，存在较高的过拟合风险。技术就绪度评估显示，大多数系统仍处于早期至中期的转化阶段，限制更多源于验证不足而非算法缺陷，再加上DBS固有的生物异质性和动态复杂性。不过，新兴的外部和前瞻性研究表明，该领域正朝着临床成熟发展，在靶向、程控、结局预测和自适应治疗递送方面具有广阔应用前景。
英文摘要
Artificial intelligence (AI) is increasingly explored across deep brain stimulation (DBS) for movement disorders, yet whether current systems are approaching deployment remains unclear. To characterise their scope, validation maturity, and translational readiness, we systematically evaluated 239 peer-reviewed studies published between 2000 and 2025, assessing AI methods, validation practices, and barriers constraining clinical translation. Research was dominated by Parkinson's disease and subthalamic nucleus targeting, with limited coverage of other disorders and targets. Most studies reported encouraging internal performance; however, external validation was rare, evaluations remained predominantly retrospective and single-centre, and more than one-quarter involved small-sample, high-dimensional datasets with elevated overfitting risk. Technology readiness assessment revealed that most systems remain at early-to-intermediate translational stages, constrained more by limited validation than by algorithmic inadequacy, compounded by the biological heterogeneity and dynamic complexity inherent to DBS. Nevertheless, emerging external and prospective studies suggest a field moving toward clinical maturity, with promising applications in targeting, programming, outcome prediction, and adaptive therapy delivery.
Journal refSouei, Z. et al. Artificial intelligence in deep brain stimulation for movement disorders: a systematic review and technology readiness assessment. npj Digit. Med. (2026)
DOI：10.1038/s41746-026-03015-4
