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We present a real-time visualization system for Transcranial MagneticStimulation (TMS), a non-invasive neuromodulation technique for treatingvarious brain disorders and mental health diseases. Our solution targets thecurrent 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 brainstimulation. The core advancement lies in our tool's real-time neuronavigationvisualization capabilities, which support clinicians in making more informeddecisions quickly and effectively. We assess our system's performance throughthree studies: First, a real-world use case scenario in a clinical setting,providing concrete feedback on applicability and usability in a practicalenvironment. Second, a comparative analysis with another TMS tool focusing oncomputational efficiency across various hardware platforms. Lastly, weconducted an expert user study to measure usability and influence in optimizingTMS treatment planning. The system is openly available for community use andfurther development on GitHub: \url{https://github.com/lorifranke/SlicerTMS}.

SlicerTMS: Real-Time Visualization of Transcranial Magnetic Stimulation for Mental Health Treatment