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Methodology

Version 4.2 by manuelmenendez on 2025/01/29 19:10

Overview

This section describes the step-by-step process used in the Neurodiagnoses project to develop a novel diagnostic framework for neurological diseases. The methodology integrates artificial intelligence (AI), biomedical ontologies, and computational neuroscience to create a structured, interpretable, and scalable diagnostic system.

1. Data Integration

Data Sources

  • Biomedical Ontologies:
    • Human Phenotype Ontology (HPO) for phenotypic abnormalities.
    • Gene Ontology (GO) for molecular and cellular processes.
  • Neuroimaging Datasets:
    • Example: Alzheimer’s Disease Neuroimaging Initiative (ADNI), OpenNeuro.
  • Clinical and Biomarker Data:
    • Anonymized clinical reports, molecular biomarkers, and test results.

Data Preprocessing

  1. Standardization: Ensure all data sources are normalized to a common format.
  2. Feature Selection: Identify relevant features for diagnosis (e.g., biomarkers, imaging scores).
  3. Data Cleaning: Handle missing values and remove duplicates.

2. AI-Based Analysis

Model Development

  • Embedding Models: Use pre-trained models like BioBERT or BioLORD for text data.
  • Classification Models:
    • Algorithms: Random Forest, Support Vector Machines (SVM), or neural networks.
    • Purpose: Predict the likelihood of specific neurological conditions based on input data.

Dimensionality Reduction and Interpretability

  • Leverage DEIBO (Data-driven Embedding Interpretation Based on Ontologies) to connect model dimensions to ontology concepts.
  • Evaluate interpretability using metrics like the Area Under the Interpretability Curve (AUIC).

3. Diagnostic Framework

Axes of Diagnosis

The framework organizes diagnostic data into three axes:

  1. Etiology: Genetic and environmental risk factors.
  2. Molecular Markers: Biomarkers such as amyloid-beta, tau, and alpha-synuclein.
  3. Neuroanatomical Correlations: Results from neuroimaging (e.g., MRI, PET).

Recommendation System

  • Suggests additional tests or biomarkers if gaps are detected in the data.
  • Prioritizes tests based on clinical impact and cost-effectiveness.

4. Computational Workflow

  1. Data Loading: Import data from storage (Drive or Bucket).
  2. Feature Engineering: Generate derived features from the raw data.
  3. Model Training:
    • Split data into training, validation, and test sets.
    • Train models with cross-validation to ensure robustness.
  4. Evaluation:
    • Metrics: Accuracy, F1-Score, AUIC for interpretability.
    • Compare against baseline models and domain benchmarks.

5. Validation

Internal Validation

  • Test the system using simulated datasets and known clinical cases.
  • Fine-tune models based on validation results.

External Validation

  • Collaborate with research institutions and hospitals to test the system in real-world settings.
  • Use anonymized patient data to ensure privacy compliance.

6. Collaborative Development

The project is open to contributions from researchers, clinicians, and developers. Key tools include:

7. Tools and Technologies

  • Programming Languages: Python for AI and data processing.
  • Frameworks:
    • TensorFlow and PyTorch for machine learning.
    • Flask or FastAPI for backend services.
  • Visualization: Plotly and Matplotlib for interactive and static visualizations.
  • EBRAINS Services:
    • Collaboratory Lab for running Notebooks.
    • Buckets for storing large datasets.