Changes for page Methodology

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--**Neurodiagnoses AI** is an open-source, AI-driven framework designed to enhance the diagnosis and prognosis of central nervous system (CNS) disorders. Building upon the Florey Dementia Index (FDI) methodology, it now encompasses a broader spectrum of neurological conditions. The system integrates multimodal data sources—including EEG, neuroimaging, biomarkers, and genetics—and employs machine learning models to deliver explainable, real-time diagnostic insights. A key feature of this framework is the incorporation of the **Generalized Neuro Biomarker Ontology Categorization (Neuromarker)**, which standardizes biomarker classification across all neurodegenerative diseases, facilitating cross-disease AI training.
++=== **Overview** ===
--**Neuromarker: Generalized Biomarker Ontology**
++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.
--Neuromarker extends the Common Alzheimer’s Disease Research Ontology (CADRO) into a comprehensive biomarker categorization framework applicable to all neurodegenerative diseases (NDDs). This ontology enables standardized classification, AI-based feature extraction, and seamless multimodal data integration.
++----
--**Core Biomarker Categories**
++=== **1. Data Integration** ===
--Within the Neurodiagnoses AI framework, biomarkers are categorized as follows:
++==== **Data Sources** ====
--|=**Category**|=**Description**
--|**Molecular Biomarkers**|Omics-based markers (genomic, transcriptomic, proteomic, metabolomic, lipidomic)
--|**Neuroimaging Biomarkers**|Structural (MRI, CT), Functional (fMRI, PET), Molecular Imaging (tau, amyloid, α-synuclein)
--|**Fluid Biomarkers**|CSF, plasma, blood-based markers for tau, amyloid, α-synuclein, TDP-43, GFAP, NfL
--|**Neurophysiological Biomarkers**|EEG, MEG, evoked potentials (ERP), sleep-related markers
--|**Digital Biomarkers**|Gait analysis, cognitive/speech biomarkers, wearables data, EHR-based markers
--|**Clinical Phenotypic Markers**|Standardized clinical scores (MMSE, MoCA, CDR, UPDRS, ALSFRS, UHDRS)
--|**Genetic Biomarkers**|Risk alleles (APOE, LRRK2, MAPT, C9orf72, PRNP) and polygenic risk scores
--|**Environmental & Lifestyle Factors**|Toxins, infections, diet, microbiome, comorbidities
++* **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.
--**Integrating External Databases into Neurodiagnoses**
--To enhance diagnostic precision, Neurodiagnoses AI incorporates data from multiple biomedical and neurological research databases. Researchers can integrate external datasets by following these steps:
++==== **Data Preprocessing** ====
--1. (((
--**Register for Access**
++1. **Standardization**: Ensure all data sources are normalized to a common format.
++1. **Feature Selection**: Identify relevant features for diagnosis (e.g., biomarkers, imaging scores).
++1. **Data Cleaning**: Handle missing values and remove duplicates.
--* Each external database requires individual registration and access approval.
--* Ensure compliance with ethical approvals and data usage agreements before integrating datasets into Neurodiagnoses.
--* Some repositories may require a Data Usage Agreement (DUA) for sensitive medical data.
--)))
--1. (((
--**Download & Prepare Data**
++----
--* Download datasets while adhering to database usage policies.
--* (((
--Ensure files meet Neurodiagnoses format requirements:
++=== **2. AI-Based Analysis** ===
--|=**Data Type**|=**Accepted Formats**
--|**Tabular Data**|.csv, .tsv
--|**Neuroimaging**|.nii, .dcm
--|**Genomic Data**|.fasta, .vcf
--|**Clinical Metadata**|.json, .xml
--)))
--* (((
--**Mandatory Fields for Integration**:
++==== **Model Development** ====
--* Subject ID: Unique patient identifier
--* Diagnosis: Standardized disease classification
--* Biomarkers: CSF, plasma, or imaging biomarkers
--* Genetic Data: Whole-genome or exome sequencing
--* Neuroimaging Metadata: MRI/PET acquisition parameters
--)))
--)))
--1. (((
--**Upload Data to Neurodiagnoses**
++* **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.
--* (((
--**Option 1: Upload to EBRAINS Bucket**
++==== **Dimensionality Reduction and Interpretability** ====
--* Location: EBRAINS Neurodiagnoses Bucket
--* Ensure correct metadata tagging before submission.
--)))
--* (((
--**Option 2: Contribute via GitHub Repository**
++* Leverage [[DEIBO>>https://drive.ebrains.eu/f/8d7157708cde4b258db0/]] (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).
--* Location: GitHub Data Repository
--* Create a new folder under /data/ and include a dataset description.
--* For large datasets, contact project administrators before uploading.
--)))
--)))
--1. (((
--**Integrate Data into AI Models**
++----
--* Open Jupyter Notebooks on EBRAINS to run preprocessing scripts.
--* Standardize neuroimaging and biomarker formats using harmonization tools.
--* Utilize machine learning models to handle missing data and feature extraction.
--* Train AI models with newly integrated patient cohorts.
++=== **3. Diagnostic Framework** ===
--**Reference**: See docs/data_processing.md for detailed instructions.
--)))
++==== **Axes of Diagnosis** ====
--**AI-Driven Biomarker Categorization**
++The framework organizes diagnostic data into three axes:
--Neurodiagnoses employs advanced AI models for biomarker classification:
++1. **Etiology**: Genetic and environmental risk factors.
++1. **Molecular Markers**: Biomarkers such as amyloid-beta, tau, and alpha-synuclein.
++1. **Neuroanatomical Correlations**: Results from neuroimaging (e.g., MRI, PET).
--|=**Model Type**|=**Application**
--|**Graph Neural Networks (GNNs)**|Identify shared biomarker pathways across diseases
--|**Contrastive Learning**|Distinguish overlapping vs. unique biomarkers
--|**Multimodal Transformer Models**|Integrate imaging, omics, and clinical data
++==== **Recommendation System** ====
--**Collaboration & Partnerships**
++* Suggests additional tests or biomarkers if gaps are detected in the data.
++* Prioritizes tests based on clinical impact and cost-effectiveness.
--Neurodiagnoses actively seeks partnerships with data providers to:
++----
--* Enable API-based data integration for real-time processing.
--* Co-develop harmonized AI-ready datasets with standardized annotations.
--* Secure funding opportunities through joint grant applications.
++=== **4. Computational Workflow** ===
--**Interested in Partnering?**
++1. **Data Loading**: Import data from storage (Drive or Bucket).
++1. **Feature Engineering**: Generate derived features from the raw data.
++1. **Model Training**:
++1*. Split data into training, validation, and test sets.
++1*. Train models with cross-validation to ensure robustness.
++1. **Evaluation**:
++1*. Metrics: Accuracy, F1-Score, AUIC for interpretability.
++1*. Compare against baseline models and domain benchmarks.
--If you represent a research consortium or database provider, reach out to explore data-sharing agreements.
++----
--**Contact**: [[info@neurodiagnoses.com>>mailto:info@neurodiagnoses.com]]
++=== **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:
++
++* **Jupyter Notebooks**: For data analysis and pipeline development.
++** Example: [[probabilistic imputation>>https://drive.ebrains.eu/f/4f69ab52f7734ef48217/]]
++* **Wiki Pages**: For documenting methods and results.
++* **Drive and Bucket**: For sharing code, data, and outputs.
++* **Collaboration with related projects: **For instance: [[//Beyond the hype: AI in dementia – from early risk detection to disease treatment//>>https://www.lethe-project.eu/beyond-the-hype-ai-in-dementia-from-early-risk-detection-to-disease-treatment/]]
++
++----
++
++=== **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.

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