We present a real-time visualization system for Transcranial Magnetic Stimulation (TMS), a non-invasive neuromodulation technique for treating various brain disorders and mental health diseases. Our solution targets the current challenges of slow and labor-intensive practices in treatment planning. Integrating Deep Learning (DL), our system rapidly predicts electric field (E-field) distributions in 0.2 seconds for precise and effective brain stimulation. The core advancement lies in our tool's real-time neuronavigation visualization capabilities, which support clinicians in making more informed decisions quickly and effectively. We assess our system's performance through three studies: First, a real-world use case scenario in a clinical setting, providing concrete feedback on applicability and usability in a practical environment. Second, a comparative analysis with another TMS tool focusing on computational efficiency across various hardware platforms. Lastly, we conducted an expert user study to measure usability and influence in optimizing TMS treatment planning. The system is openly available for community use and further development on GitHub: \url{https://github.com/lorifranke/SlicerTMS}.

翻译：我们提出了一个经颅磁刺激（TMS）的实时可视化系统，TMS是一种非侵入性神经调控技术，用于治疗多种脑部疾病和心理健康疾病。我们的解决方案针对当前治疗规划中缓慢且劳动密集的实践挑战。通过集成深度学习（DL），该系统能在0.2秒内快速预测电场（E-field）分布，从而实现精确有效的脑刺激。核心进展在于该工具的实时神经导航可视化能力，能够支持临床医生快速有效地做出更明智的决策。通过三项研究评估系统性能：第一，在临床环境中进行真实场景应用案例研究，提供关于实用环境中适用性和可用性的具体反馈；第二，与另一款TMS工具进行对比分析，重点关注跨多种硬件平台的计算效率；最后，进行专家用户研究，以衡量其在优化TMS治疗规划中的可用性和影响力。该系统已在GitHub上公开供社区使用和进一步开发：\url{https://github.com/lorifranke/SlicerTMS}。