Wiki source code of Methodology

Version 10.1 by manuelmenendez on 2025/02/01 18:31
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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**.
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 ----
22
23 === **1. Data Integration** ===
24
25 ==== **Data Sources** ====
26
27 **Biomedical Ontologies & Databases:**
28
29 * **Human Phenotype Ontology (HPO)** for symptom annotation.
30 * **Gene Ontology (GO)** for molecular and cellular processes.
31
32 **Dimensionality Reduction and Interpretability:**
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 ----
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 === **2. AI-Based Analysis** ===
64
65 ==== **Machine Learning & Deep Learning Models** ====
66
67 **Risk Prediction Models:**
68
69 * **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
70
71 **Biomarker Classification & Probabilistic Imputation:**
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 ----
86
87 === **3. Diagnostic Framework & Clinical Decision Support** ===
88
89 ==== **Tridimensional Diagnostic Axes** ====
90
91 **Axis 1: Etiology (Pathogenic Mechanisms)**
92
93 * Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
94 * **AI-assisted annotation** provides **causal interpretations** for clinical use.
95
96 **Axis 2: Molecular Markers & Biomarkers**
97
98 * **Integration of CSF, blood, and neuroimaging biomarkers**.
99 * **Structured annotation** highlights **biological pathways linked to diagnosis**.
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 ----
107
108 === **4. Computational Workflow & Annotation Pipelines** ===
109
110 ==== **Data Processing Steps** ====
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 ----
132
133 === **5. Validation & Real-World Testing** ===
134
135 ==== **Prospective Clinical Study** ====
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**.
140
141 ==== **Quality Assurance & Explainability** ====
142
143 * **Annotations linked to structured knowledge graphs** for improved transparency.
144 * **Interactive annotation editor** allows clinicians to validate AI outputs.
145
146 ----
147
148 === **6. Collaborative Development** ===
149
150 The project is **open to contributions** from **researchers, clinicians, and developers**.
151
152 **Key tools include:**
153
154 * **Jupyter Notebooks**: For data analysis and pipeline development.
155 ** Example: **probabilistic imputation**
156 * **Wiki Pages**: For documenting methods and results.
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
161 ----
162
163 === **7. Tools and Technologies** ===
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.**