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Changes for page Methodology

Last modified by manuelmenendez on 2025/03/14 08:31
From version 10.1
edited by manuelmenendez
on 2025/02/01 18:31
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To version 4.1
edited by manuelmenendez
on 2025/01/27 23:46
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Summary

Details

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1 -==== **Overview** ====
1 +=== **Overview** ===
2 2  
3 -This project develops a **tridimensional diagnostic framework** for **CNS diseases**, incorporating **AI-powered annotation tools** to improve **interpretability, standardization, and clinical utility**. The methodology integrates **multi-modal data**, including **genetic, neuroimaging, neurophysiological, and biomarker datasets**, and applies **machine learning models** to generate **structured, explainable diagnostic outputs**.
3 +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.
4 4  
5 -=== **Workflow** ===
6 -
7 -1. (((
8 -**We Use GitHub to [[Store and develop AI models, scripts, and annotation pipelines.>>https://github.com/users/manuelmenendezgonzalez/projects/1/views/1]]**
9 -
10 -* Create a **GitHub repository** for AI scripts and models.
11 -* Use **GitHub Projects** to manage research milestones.
12 -)))
13 -1. (((
14 -**We Use EBRAINS for Data & Collaboration**
15 -
16 -* Store **biomarker and neuroimaging data** in **EBRAINS Buckets**.
17 -* Run **Jupyter Notebooks** in **EBRAINS Lab** to test AI models.
18 -* Use **EBRAINS Wiki** for structured documentation and research discussion.
19 -)))
20 -
21 21  ----
22 22  
23 23  === **1. Data Integration** ===
... ... @@ -24,166 +24,100 @@
24 24  
25 25  ==== **Data Sources** ====
26 26  
27 -**Biomedical Ontologies & Databases:**
11 +* **Biomedical Ontologies**:
12 +** Human Phenotype Ontology (HPO) for phenotypic abnormalities.
13 +** Gene Ontology (GO) for molecular and cellular processes.
14 +* **Neuroimaging Datasets**:
15 +** Example: Alzheimer’s Disease Neuroimaging Initiative (ADNI), OpenNeuro.
16 +* **Clinical and Biomarker Data**:
17 +** Anonymized clinical reports, molecular biomarkers, and test results.
28 28  
29 -* **Human Phenotype Ontology (HPO)** for symptom annotation.
30 -* **Gene Ontology (GO)** for molecular and cellular processes.
19 +==== **Data Preprocessing** ====
31 31  
32 -**Dimensionality Reduction and Interpretability:**
21 +1. **Standardization**: Ensure all data sources are normalized to a common format.
22 +1. **Feature Selection**: Identify relevant features for diagnosis (e.g., biomarkers, imaging scores).
23 +1. **Data Cleaning**: Handle missing values and remove duplicates.
33 33  
34 -* **Evaluate interpretability** using metrics like the **Area Under the Interpretability Curve (AUIC)**.
35 -* **Leverage DEIBO (Data-driven Embedding Interpretation Based on Ontologies)** to connect model dimensions to ontology concepts.
36 -
37 -**Neuroimaging & EEG/MEG Data:**
38 -
39 -* **MRI volumetric measures** for brain atrophy tracking.
40 -* **EEG functional connectivity patterns** (AI-Mind).
41 -
42 -**Clinical & Biomarker Data:**
43 -
44 -* **CSF biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
45 -* **Sleep monitoring and actigraphy data** (ADIS).
46 -
47 -**Federated Learning Integration:**
48 -
49 -* **Secure multi-center data harmonization** (PROMINENT).
50 -
51 51  ----
52 52  
53 -==== **Annotation System for Multi-Modal Data** ====
54 -
55 -To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
56 -
57 -* **Assign standardized metadata tags** to diagnostic features.
58 -* **Provide contextual explanations** for AI-based classifications.
59 -* **Track temporal disease progression annotations** to identify long-term trends.
60 -
61 -----
62 -
63 63  === **2. AI-Based Analysis** ===
64 64  
65 -==== **Machine Learning & Deep Learning Models** ====
29 +==== **Model Development** ====
66 66  
67 -**Risk Prediction Models:**
31 +* **Embedding Models**: Use pre-trained models like BioBERT or BioLORD for text data.
32 +* **Classification Models**:
33 +** Algorithms: Random Forest, Support Vector Machines (SVM), or neural networks.
34 +** Purpose: Predict the likelihood of specific neurological conditions based on input data.
68 68  
69 -* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
36 +==== **Dimensionality Reduction and Interpretability** ====
70 70  
71 -**Biomarker Classification & Probabilistic Imputation:**
38 +* Leverage [[DEIBO>>https://drive.ebrains.eu/f/8d7157708cde4b258db0/]] (Data-driven Embedding Interpretation Based on Ontologies) to connect model dimensions to ontology concepts.
39 +* Evaluate interpretability using metrics like the Area Under the Interpretability Curve (AUIC).
72 72  
73 -* **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
74 -
75 -**Neuroimaging Feature Extraction:**
76 -
77 -* **MRI & EEG data** annotated with **neuroanatomical feature labels**.
78 -
79 -==== **AI-Powered Annotation System** ====
80 -
81 -* Uses **SHAP-based interpretability tools** to explain model decisions.
82 -* Generates **automated clinical annotations** in structured reports.
83 -* Links findings to **standardized medical ontologies** (e.g., **SNOMED, HPO**).
84 -
85 85  ----
86 86  
87 -=== **3. Diagnostic Framework & Clinical Decision Support** ===
43 +=== **3. Diagnostic Framework** ===
88 88  
89 -==== **Tridimensional Diagnostic Axes** ====
45 +==== **Axes of Diagnosis** ====
90 90  
91 -**Axis 1: Etiology (Pathogenic Mechanisms)**
47 +The framework organizes diagnostic data into three axes:
92 92  
93 -* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
94 -* **AI-assisted annotation** provides **causal interpretations** for clinical use.
49 +1. **Etiology**: Genetic and environmental risk factors.
50 +1. **Molecular Markers**: Biomarkers such as amyloid-beta, tau, and alpha-synuclein.
51 +1. **Neuroanatomical Correlations**: Results from neuroimaging (e.g., MRI, PET).
95 95  
96 -**Axis 2: Molecular Markers & Biomarkers**
53 +==== **Recommendation System** ====
97 97  
98 -* **Integration of CSF, blood, and neuroimaging biomarkers**.
99 -* **Structured annotation** highlights **biological pathways linked to diagnosis**.
55 +* Suggests additional tests or biomarkers if gaps are detected in the data.
56 +* Prioritizes tests based on clinical impact and cost-effectiveness.
100 100  
101 -**Axis 3: Neuroanatomoclinical Correlations**
102 -
103 -* **MRI and EEG data** provide anatomical and functional insights.
104 -* **AI-generated progression maps** annotate **brain structure-function relationships**.
105 -
106 106  ----
107 107  
108 -=== **4. Computational Workflow & Annotation Pipelines** ===
60 +=== **4. Computational Workflow** ===
109 109  
110 -==== **Data Processing Steps** ====
62 +1. **Data Loading**: Import data from storage (Drive or Bucket).
63 +1. **Feature Engineering**: Generate derived features from the raw data.
64 +1. **Model Training**:
65 +1*. Split data into training, validation, and test sets.
66 +1*. Train models with cross-validation to ensure robustness.
67 +1. **Evaluation**:
68 +1*. Metrics: Accuracy, F1-Score, AUIC for interpretability.
69 +1*. Compare against baseline models and domain benchmarks.
111 111  
112 -**Data Ingestion:**
113 -
114 -* **Harmonized datasets** stored in **EBRAINS Bucket**.
115 -* **Preprocessing pipelines** clean and standardize data.
116 -
117 -**Feature Engineering:**
118 -
119 -* **AI models** extract **clinically relevant patterns** from **EEG, MRI, and biomarkers**.
120 -
121 -**AI-Generated Annotations:**
122 -
123 -* **Automated tagging** of diagnostic features in **structured reports**.
124 -* **Explainability modules (SHAP, LIME)** ensure transparency in predictions.
125 -
126 -**Clinical Decision Support Integration:**
127 -
128 -* **AI-annotated findings** fed into **interactive dashboards**.
129 -* **Clinicians can adjust, validate, and modify annotations**.
130 -
131 131  ----
132 132  
133 -=== **5. Validation & Real-World Testing** ===
73 +=== **5. Validation** ===
134 134  
135 -==== **Prospective Clinical Study** ====
75 +==== **Internal Validation** ====
136 136  
137 -* **Multi-center validation** of AI-based **annotations & risk stratifications**.
138 -* **Benchmarking against clinician-based diagnoses**.
139 -* **Real-world testing** of AI-powered **structured reporting**.
77 +* Test the system using simulated datasets and known clinical cases.
78 +* Fine-tune models based on validation results.
140 140  
141 -==== **Quality Assurance & Explainability** ====
80 +==== **External Validation** ====
142 142  
143 -* **Annotations linked to structured knowledge graphs** for improved transparency.
144 -* **Interactive annotation editor** allows clinicians to validate AI outputs.
82 +* Collaborate with research institutions and hospitals to test the system in real-world settings.
83 +* Use anonymized patient data to ensure privacy compliance.
145 145  
146 146  ----
147 147  
148 148  === **6. Collaborative Development** ===
149 149  
150 -The project is **open to contributions** from **researchers, clinicians, and developers**.
89 +The project is open to contributions from researchers, clinicians, and developers. Key tools include:
151 151  
152 -**Key tools include:**
153 -
154 154  * **Jupyter Notebooks**: For data analysis and pipeline development.
155 -** Example: **probabilistic imputation**
92 +** Example: [[probabilistic imputation>>https://drive.ebrains.eu/f/4f69ab52f7734ef48217/]]
156 156  * **Wiki Pages**: For documenting methods and results.
157 157  * **Drive and Bucket**: For sharing code, data, and outputs.
158 -* **Collaboration with related projects**:
159 -** Example: **Beyond the hype: AI in dementia – from early risk detection to disease treatment**
160 160  
161 161  ----
162 162  
163 163  === **7. Tools and Technologies** ===
164 164  
165 -==== **Programming Languages:** ====
166 -
167 -* **Python** for AI and data processing.
168 -
169 -==== **Frameworks:** ====
170 -
171 -* **TensorFlow** and **PyTorch** for machine learning.
172 -* **Flask** or **FastAPI** for backend services.
173 -
174 -==== **Visualization:** ====
175 -
176 -* **Plotly** and **Matplotlib** for interactive and static visualizations.
177 -
178 -==== **EBRAINS Services:** ====
179 -
180 -* **Collaboratory Lab** for running Notebooks.
181 -* **Buckets** for storing large datasets.
182 -
183 -----
184 -
185 -=== **Why This Matters** ===
186 -
187 -* **The annotation system ensures that AI-generated insights are structured, interpretable, and clinically meaningful.**
188 -* **It enables real-time tracking of disease progression across the three diagnostic axes.**
189 -* **It facilitates integration with electronic health records and decision-support tools, improving AI adoption in clinical workflows.**
100 +* **Programming Languages**: Python for AI and data processing.
101 +* **Frameworks**:
102 +** TensorFlow and PyTorch for machine learning.
103 +** Flask or FastAPI for backend services.
104 +* **Visualization**: Plotly and Matplotlib for interactive and static visualizations.
105 +* **EBRAINS Services**:
106 +** Collaboratory Lab for running Notebooks.
107 +** Buckets for storing large datasets.