The NeoSmartML Foundation Model project aims to develop a comprehensive deep learning model that can extract, analyze, and predict various clinical outcomes from neonatal ICU monitor images captured through ESP32-CAM devices. Building upon existing work in fusion models and streaming data analysis, this project extends beyond basic risk classification to create a more versatile foundation model capable of multiple downstream tasks. Look here for earlier architecture: Machine learning-based decision-support.
This application will process real-time monitor images through OCR to extract vital signs data, combining temporal sequences with clinical context to provide various predictions and insights for neonatal care. The foundation model will serve as a base for multiple specialized tasks through fine-tuning or prompt engineering.
Model Architecture and Tasks:
The model will utilize a hierarchical architecture combining:
- Vision encoder for raw monitor image processing
- OCR processing layer for text extraction
- Temporal sequence modeling for trend analysis
- Multi-task prediction heads for various clinical outcomes
Target Classification Tasks:
Risk Level Assessment
- Low, moderate, and high-risk classifications
- Continuous risk probability scoring
- Trend-based risk projection
Clinical Event Prediction
- Apnea episodes
- Bradycardia events
- Desaturation events
- Temperature instability
Treatment Response Prediction
- Oxygen therapy effectiveness
- Temperature intervention outcomes
- Feeding tolerance
- Medication response patterns
Physiological State Classification
- Sleep/wake cycles
- Stress levels
- Pain assessment
- Respiratory effort patterns
The deliverables of the project are as follows:
- Develop a foundation model architecture that handles both image and temporal data
- Create specialized prediction heads for multiple clinical tasks
- Implement efficient model compression for edge deployment
- Build an evaluation framework for model performance
- Create documentation for model usage and fine-tuning
- Develop integration guidelines for clinical applications
This foundation model will significantly advance the capabilities of neonatal monitoring systems by providing a versatile base model that can be adapted for multiple clinical tasks. The edge-optimized implementation ensures practical deployment in resource-constrained environments while maintaining high accuracy and real-time processing capabilities.
The model’s ability to handle multiple tasks through a single foundation architecture will improve efficiency and reduce computational overhead, while the attention-based mechanisms will help identify critical patterns in vital signs data. The project’s focus on interpretability and clinical integration ensures that the model’s predictions can be effectively utilized in clinical practice.
Project size: Large (~350 hours)
Mentors: @sunbiz and @shbucher