1. AI-Driven Stem Cell Behavior Prediction
Research Focus: Develop ML models to predict stem cell differentiation, migration, and integration patterns in epileptic brains
- Use reinforcement learning to simulate how transplanted cells interact with damaged neural circuits
- Create generative AI models to forecast long-term therapeutic outcomes (e.g., seizure suppression, tissue repair)
Application: Optimize transplantation protocols and reduce trial-and-error in animal models
2. Neural Circuit Mapping & Integration Analysis
Research Focus: Build AI tools to decode how transplanted stem cells form functional connections in epileptic circuits
- Combine electrophysiology data (e.g., EEG) with microscopy images to map synaptic integration
- Train graph neural networks (GNNs) to model circuit rewiring post-transplantation
Application: Identify biomarkers of successful integration to refine therapies
3. Enhanced Seizure Prediction via Multimodal AI
Research Focus: Improve Aurevia's predictive accuracy by integrating diverse data streams
- Train transformer-based models on EEG, wearable sensor data, and patient-specific biomarkers
- Use federated learning to enhance personalization while preserving privacy
Application: Reduce false alarms and extend prediction windows beyond 15 minutes
4. Real-Time Stem Cell Imaging & Quality Control
Research Focus: Upgrade image analysis pipelines with real-time AI
- Develop lightweight vision transformers for 3D microscopy video analysis
- Implement anomaly detection to flag low-quality cell batches or aberrant differentiation
Application: Accelerate preclinical research and ensure consistency in transplantation
5. Closed-Loop Brain Repair Systems
Research Focus: Combine Aurevia's seizure prediction with automated therapeutic interventions
- Design AI-controlled "smart implants" for localized stem cell activation during pre-seizure states
- Simulate dynamic brain repair using digital twins of epileptic circuits
Application: Move from passive monitoring to active, AI-driven repair
6. Generative Models for Brain Tissue Reconstruction
Research Focus: Use diffusion models/GANs to simulate neural repair processes
- Predict optimal stem cell delivery sites based on lesion location
- Generate synthetic brain images to train analysis models with limited data
Application: Guide surgical planning and reduce invasive animal trials
7. Ethical AI for Personalized Epilepsy Care
Research Focus: Ensure fairness and transparency in AI-driven diagnostics/therapies
- Audit datasets for biases in epilepsy subtype representation
- Develop explainable AI (XAI) tools for clinical interpretation
Application: Build trust in AI-guided treatments among patients and regulators
8. Translational AI for Clinical Deployment
Research Focus: Optimize AI models for edge computing in wearables
- Use neuromorphic computing or quantization to reduce power consumption
- Design federated learning frameworks for continuous model updates
Application: Enable real-time, low-latency seizure prediction on wearable devices