Wiki source code of Methodology

Version 6.1 by manuelmenendez on 2025/02/01 11:57
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1 ==== **Overview** ====
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**.
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5 ----
6
7 === **1. Data Integration** ===
8
9 ==== **Data Sources** ====
10
11 **Biomedical Ontologies & Databases:**
12
13 * **Human Phenotype Ontology (HPO)** for symptom annotation.
14 * **Gene Ontology (GO)** for molecular and cellular processes.
15
16 **Dimensionality Reduction and Interpretability:**
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.
20
21 **Neuroimaging & EEG/MEG Data:**
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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).
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35 ----
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:
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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.
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45 ----
46
47 === **2. AI-Based Analysis** ===
48
49 ==== **Machine Learning & Deep Learning Models** ====
50
51 **Risk Prediction Models:**
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53 * **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
54
55 **Biomarker Classification & Probabilistic Imputation:**
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57 * **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
58
59 **Neuroimaging Feature Extraction:**
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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**).
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69 ----
70
71 === **3. Diagnostic Framework & Clinical Decision Support** ===
72
73 ==== **Tridimensional Diagnostic Axes** ====
74
75 **Axis 1: Etiology (Pathogenic Mechanisms)**
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77 * Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
78 * **AI-assisted annotation** provides **causal interpretations** for clinical use.
79
80 **Axis 2: Molecular Markers & Biomarkers**
81
82 * **Integration of CSF, blood, and neuroimaging biomarkers**.
83 * **Structured annotation** highlights **biological pathways linked to diagnosis**.
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**.
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90 ----
91
92 === **4. Computational Workflow & Annotation Pipelines** ===
93
94 ==== **Data Processing Steps** ====
95
96 **Data Ingestion:**
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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:**
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107 * **Automated tagging** of diagnostic features in **structured reports**.
108 * **Explainability modules (SHAP, LIME)** ensure transparency in predictions.
109
110 **Clinical Decision Support Integration:**
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112 * **AI-annotated findings** fed into **interactive dashboards**.
113 * **Clinicians can adjust, validate, and modify annotations**.
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115 ----
116
117 === **5. Validation & Real-World Testing** ===
118
119 ==== **Prospective Clinical Study** ====
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**.
124
125 ==== **Quality Assurance & Explainability** ====
126
127 * **Annotations linked to structured knowledge graphs** for improved transparency.
128 * **Interactive annotation editor** allows clinicians to validate AI outputs.
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130 ----
131
132 === **6. Collaborative Development** ===
133
134 The project is **open to contributions** from **researchers, clinicians, and developers**.
135
136 **Key tools include:**
137
138 * **Jupyter Notebooks**: For data analysis and pipeline development.
139 ** Example: **probabilistic imputation**
140 * **Wiki Pages**: For documenting methods and results.
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**
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145 ----
146
147 === **7. Tools and Technologies** ===
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.**