Neurotwin Ai

• Author(s):

  Suguna M | Harish V | Deepak S | Jai Sarvesh N | Guhan D

• Keywords:

• Abstract:

  NeuroTwin AI Is An Intelligent Healthcare System Designed To Build A Personalized Digital Twin Of The Human Brain. At Present, Treatment Planning For Brain-related Diseases Such As Epilepsy, Alzheimer’s, And Parkinson’s Relies Heavily On Trial-and-error Methods, Where Doctors Prescribe Medications Or Suggest Procedures Without Being Able To Simulate Outcomes In Advance. This Uncertainty Often Leads To Ineffective Treatments, Risks To Patient Safety, And Increased Healthcare Costs. NeuroTwin AI Addresses This Challenge By Creating A Virtual Brain Model Using MRI, FMRI, EEG, And Clinical Data, Allowing Doctors To Simulate Therapies Such As Medications, Deep Brain Stimulation (DBS), Transcranial Magnetic Stimulation (TMS), And Surgical Procedures In A Safe Digital Environment. The System Also Employs Explainable Artificial Intelligence (XAI) Techniques Like SHAP, LIME, And Grad-CAM, Ensuring That Every Prediction And Recommendation Is Accompanied By Clear Reasoning, Thereby Improving Doctor Trust And Transparency. The Development Of This System Follows A Structured Methodology Involving Communication With Clinicians, Planning Of Modules, Data Modeling, And Deployment Of AI-based Simulations. It Is Built Using Advanced Algorithms Such As Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), State-Space Models, And Reinforcement Learning, Supported By Federated Learning To Ensure Patient Privacy. Testing Is Carried Out Through Validation Against Real Patient Data And Clinician Feedback. The Result Of This Project Is A Prototype Of NeuroTwin AI, Which Enables Doctors To Explore Treatment Outcomes Virtually, Reduce Risks, Improve Decision-making, And Save Time And Resources. This System Has The Potential To Transform The Way Neurological Treatments Are Planned And Delivered, Offering Safer, More Effective, And Personalized Healthcare

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Other Details

• Paper id:

  IJSARTV11I10104071

• Published in:

  Volume: 11 Issue: 10 October 2025

• Publication Date:

  2025-10-03

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