Documentation & Resources
Comprehensive information about SOLVIA research, datasets, and publications
Research Methodology
SOLVIA employs a sophisticated multimodal architecture that processes different types of medical data to provide comprehensive skin health analysis.
AI Architecture
- CNNConvolutional Neural Networks for dermoscopic and clinical image analysis
- MLPMultilayer Perceptrons for processing clinical laboratory results
- FUSIONAdvanced multimodal fusion techniques to integrate diverse data types
- MULTIMultilabel classification for simultaneous risk assessment
Research Process
- Dataset curation and preprocessing from public sources
- Model architecture design and implementation
- Training on diverse patient populations
- Validation using cross-validation techniques
- Performance evaluation using standard metrics
- Error analysis and model refinement
- Documentation and reporting of findings
- Preparation for peer review and publication
Research Areas
SOLVIA focuses on three critical healthcare challenges related to skin health and early disease detection.
Skin Cancer Detection
Using dermoscopic images to identify malignant skin lesions at early stages, potentially improving treatment outcomes and survival rates.
Autoimmune Disorder Forecasting
Analyzing clinical data patterns to predict the onset of autoimmune diseases before traditional diagnostic criteria are met.
Adverse Drug Reaction Prediction
Identifying patients at risk for severe cutaneous adverse reactions to medications based on their clinical profile.