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Changes for page Methodology

Last modified by manuelmenendez on 2025/03/14 08:31
From version 6.1
edited by manuelmenendez
on 2025/02/01 11:57
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To version 4.3
edited by manuelmenendez
on 2025/01/29 19:11
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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 5  ----
6 6  
... ... @@ -8,166 +8,102 @@
8 8  
9 9  ==== **Data Sources** ====
10 10  
11 -**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.
12 12  
13 -* **Human Phenotype Ontology (HPO)** for symptom annotation.
14 -* **Gene Ontology (GO)** for molecular and cellular processes.
15 15  
16 -**Dimensionality Reduction and Interpretability:**
20 +==== **Data Preprocessing** ====
17 17  
18 -* **Evaluate interpretability** using metrics like the **Area Under the Interpretability Curve (AUIC)**.
19 -* **Leverage DEIBO (Data-driven Embedding Interpretation Based on Ontologies)** to connect model dimensions to ontology concepts.
22 +1. **Standardization**: Ensure all data sources are normalized to a common format.
23 +1. **Feature Selection**: Identify relevant features for diagnosis (e.g., biomarkers, imaging scores).
24 +1. **Data Cleaning**: Handle missing values and remove duplicates.
20 20  
21 -**Neuroimaging & EEG/MEG Data:**
22 -
23 -* **MRI volumetric measures** for brain atrophy tracking.
24 -* **EEG functional connectivity patterns** (AI-Mind).
25 -
26 -**Clinical & Biomarker Data:**
27 -
28 -* **CSF biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
29 -* **Sleep monitoring and actigraphy data** (ADIS).
30 -
31 -**Federated Learning Integration:**
32 -
33 -* **Secure multi-center data harmonization** (PROMINENT).
34 -
35 35  ----
36 36  
37 -==== **Annotation System for Multi-Modal Data** ====
38 -
39 -To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
40 -
41 -* **Assign standardized metadata tags** to diagnostic features.
42 -* **Provide contextual explanations** for AI-based classifications.
43 -* **Track temporal disease progression annotations** to identify long-term trends.
44 -
45 -----
46 -
47 47  === **2. AI-Based Analysis** ===
48 48  
49 -==== **Machine Learning & Deep Learning Models** ====
30 +==== **Model Development** ====
50 50  
51 -**Risk Prediction Models:**
32 +* **Embedding Models**: Use pre-trained models like BioBERT or BioLORD for text data.
33 +* **Classification Models**:
34 +** Algorithms: Random Forest, Support Vector Machines (SVM), or neural networks.
35 +** Purpose: Predict the likelihood of specific neurological conditions based on input data.
52 52  
53 -* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
37 +==== **Dimensionality Reduction and Interpretability** ====
54 54  
55 -**Biomarker Classification & Probabilistic Imputation:**
39 +* Leverage [[DEIBO>>https://drive.ebrains.eu/f/8d7157708cde4b258db0/]] (Data-driven Embedding Interpretation Based on Ontologies) to connect model dimensions to ontology concepts.
40 +* Evaluate interpretability using metrics like the Area Under the Interpretability Curve (AUIC).
56 56  
57 -* **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
58 -
59 -**Neuroimaging Feature Extraction:**
60 -
61 -* **MRI & EEG data** annotated with **neuroanatomical feature labels**.
62 -
63 -==== **AI-Powered Annotation System** ====
64 -
65 -* Uses **SHAP-based interpretability tools** to explain model decisions.
66 -* Generates **automated clinical annotations** in structured reports.
67 -* Links findings to **standardized medical ontologies** (e.g., **SNOMED, HPO**).
68 -
69 69  ----
70 70  
71 -=== **3. Diagnostic Framework & Clinical Decision Support** ===
44 +=== **3. Diagnostic Framework** ===
72 72  
73 -==== **Tridimensional Diagnostic Axes** ====
46 +==== **Axes of Diagnosis** ====
74 74  
75 -**Axis 1: Etiology (Pathogenic Mechanisms)**
48 +The framework organizes diagnostic data into three axes:
76 76  
77 -* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
78 -* **AI-assisted annotation** provides **causal interpretations** for clinical use.
50 +1. **Etiology**: Genetic and environmental risk factors.
51 +1. **Molecular Markers**: Biomarkers such as amyloid-beta, tau, and alpha-synuclein.
52 +1. **Neuroanatomical Correlations**: Results from neuroimaging (e.g., MRI, PET).
79 79  
80 -**Axis 2: Molecular Markers & Biomarkers**
54 +==== **Recommendation System** ====
81 81  
82 -* **Integration of CSF, blood, and neuroimaging biomarkers**.
83 -* **Structured annotation** highlights **biological pathways linked to diagnosis**.
56 +* Suggests additional tests or biomarkers if gaps are detected in the data.
57 +* Prioritizes tests based on clinical impact and cost-effectiveness.
84 84  
85 -**Axis 3: Neuroanatomoclinical Correlations**
86 -
87 -* **MRI and EEG data** provide anatomical and functional insights.
88 -* **AI-generated progression maps** annotate **brain structure-function relationships**.
89 -
90 90  ----
91 91  
92 -=== **4. Computational Workflow & Annotation Pipelines** ===
61 +=== **4. Computational Workflow** ===
93 93  
94 -==== **Data Processing Steps** ====
63 +1. **Data Loading**: Import data from storage (Drive or Bucket).
64 +1. **Feature Engineering**: Generate derived features from the raw data.
65 +1. **Model Training**:
66 +1*. Split data into training, validation, and test sets.
67 +1*. Train models with cross-validation to ensure robustness.
68 +1. **Evaluation**:
69 +1*. Metrics: Accuracy, F1-Score, AUIC for interpretability.
70 +1*. Compare against baseline models and domain benchmarks.
95 95  
96 -**Data Ingestion:**
97 -
98 -* **Harmonized datasets** stored in **EBRAINS Bucket**.
99 -* **Preprocessing pipelines** clean and standardize data.
100 -
101 -**Feature Engineering:**
102 -
103 -* **AI models** extract **clinically relevant patterns** from **EEG, MRI, and biomarkers**.
104 -
105 -**AI-Generated Annotations:**
106 -
107 -* **Automated tagging** of diagnostic features in **structured reports**.
108 -* **Explainability modules (SHAP, LIME)** ensure transparency in predictions.
109 -
110 -**Clinical Decision Support Integration:**
111 -
112 -* **AI-annotated findings** fed into **interactive dashboards**.
113 -* **Clinicians can adjust, validate, and modify annotations**.
114 -
115 115  ----
116 116  
117 -=== **5. Validation & Real-World Testing** ===
74 +=== **5. Validation** ===
118 118  
119 -==== **Prospective Clinical Study** ====
76 +==== **Internal Validation** ====
120 120  
121 -* **Multi-center validation** of AI-based **annotations & risk stratifications**.
122 -* **Benchmarking against clinician-based diagnoses**.
123 -* **Real-world testing** of AI-powered **structured reporting**.
78 +* Test the system using simulated datasets and known clinical cases.
79 +* Fine-tune models based on validation results.
124 124  
125 -==== **Quality Assurance & Explainability** ====
81 +==== **External Validation** ====
126 126  
127 -* **Annotations linked to structured knowledge graphs** for improved transparency.
128 -* **Interactive annotation editor** allows clinicians to validate AI outputs.
83 +* Collaborate with research institutions and hospitals to test the system in real-world settings.
84 +* Use anonymized patient data to ensure privacy compliance.
129 129  
130 130  ----
131 131  
132 132  === **6. Collaborative Development** ===
133 133  
134 -The project is **open to contributions** from **researchers, clinicians, and developers**.
90 +The project is open to contributions from researchers, clinicians, and developers. Key tools include:
135 135  
136 -**Key tools include:**
137 -
138 138  * **Jupyter Notebooks**: For data analysis and pipeline development.
139 -** Example: **probabilistic imputation**
93 +** Example: [[probabilistic imputation>>https://drive.ebrains.eu/f/4f69ab52f7734ef48217/]]
140 140  * **Wiki Pages**: For documenting methods and results.
141 141  * **Drive and Bucket**: For sharing code, data, and outputs.
142 -* **Collaboration with related projects**:
143 -** Example: **Beyond the hype: AI in dementia – from early risk detection to disease treatment**
96 +* **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/]]
144 144  
145 145  ----
146 146  
147 147  === **7. Tools and Technologies** ===
148 148  
149 -==== **Programming Languages:** ====
150 -
151 -* **Python** for AI and data processing.
152 -
153 -==== **Frameworks:** ====
154 -
155 -* **TensorFlow** and **PyTorch** for machine learning.
156 -* **Flask** or **FastAPI** for backend services.
157 -
158 -==== **Visualization:** ====
159 -
160 -* **Plotly** and **Matplotlib** for interactive and static visualizations.
161 -
162 -==== **EBRAINS Services:** ====
163 -
164 -* **Collaboratory Lab** for running Notebooks.
165 -* **Buckets** for storing large datasets.
166 -
167 -----
168 -
169 -=== **Why This Matters** ===
170 -
171 -* **The annotation system ensures that AI-generated insights are structured, interpretable, and clinically meaningful.**
172 -* **It enables real-time tracking of disease progression across the three diagnostic axes.**
173 -* **It facilitates integration with electronic health records and decision-support tools, improving AI adoption in clinical workflows.**
102 +* **Programming Languages**: Python for AI and data processing.
103 +* **Frameworks**:
104 +** TensorFlow and PyTorch for machine learning.
105 +** Flask or FastAPI for backend services.
106 +* **Visualization**: Plotly and Matplotlib for interactive and static visualizations.
107 +* **EBRAINS Services**:
108 +** Collaboratory Lab for running Notebooks.
109 +** Buckets for storing large datasets.