Wiki source code of Methodology

Version 15.1 by manuelmenendez on 2025/02/09 10:08
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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** ==
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7 1. (((
8 **We Use GitHub to [[Store and develop AI models, scripts, and annotation pipelines.>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]]**
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 === **EBRAINS Medical Informatics Platform (MIP)**. ===
26
27 Neurodiagnoses integrates clinical data via the **EBRAINS Medical Informatics Platform (MIP)**. MIP federates decentralized clinical data, allowing Neurodiagnoses to securely access and process sensitive information for AI-based diagnostics.
28
29 ==== How It Works ====
30
31
32 1. (((
33 **Authentication & API Access:**
34
35 * Users must have an **EBRAINS account**.
36 * Neurodiagnoses uses **secure API endpoints** to fetch clinical data (e.g., from the **Federation for Dementia**).
37 )))
38 1. (((
39 **Data Mapping & Harmonization:**
40
41 * Retrieved data is **normalized** and converted to standard formats (.csv, .json).
42 * Data from **multiple sources** is harmonized to ensure consistency for AI processing.
43 )))
44 1. (((
45 **Security & Compliance:**
46
47 * All data access is **logged and monitored**.
48 * Data remains on **MIP servers** using **federated learning techniques** when possible.
49 * Access is granted only after signing a **Data Usage Agreement (DUA)**.
50 )))
51
52 ==== Implementation Steps ====
53
54
55 1. Clone the repository.
56 1. Configure your **EBRAINS API credentials** in mip_integration.py.
57 1. Run the script to **download and harmonize clinical data**.
58 1. Process the data for **AI model training**.
59
60 For more detailed instructions, please refer to the **[[MIP Documentation>>url:https://mip.ebrains.eu/]]**.
61
62 ----
63
64 === Data Processing & Integration with Clinica.Run ===
65
66 Neurodiagnoses now supports **Clinica.Run**, an open-source neuroimaging platform designed for **multimodal data processing and reproducible neuroscience workflows**.
67
68 ==== How It Works ====
69
70
71 1. (((
72 **Neuroimaging Preprocessing:**
73
74 * MRI, PET, EEG data is preprocessed using **Clinica.Run pipelines**.
75 * Supports **longitudinal and cross-sectional analyses**.
76 )))
77 1. (((
78 **Automated Biomarker Extraction:**
79
80 * Standardized extraction of **volumetric, metabolic, and functional biomarkers**.
81 * Integration with machine learning models in Neurodiagnoses.
82 )))
83 1. (((
84 **Data Security & Compliance:**
85
86 * Clinica.Run operates in **compliance with GDPR and HIPAA**.
87 * Neuroimaging data remains **within the original storage environment**.
88 )))
89
90 ==== Implementation Steps ====
91
92
93 1. Install **Clinica.Run** dependencies.
94 1. Configure your **Clinica.Run pipeline** in clinica_run_config.json.
95 1. Run the pipeline for **preprocessing and biomarker extraction**.
96 1. Use processed neuroimaging data for **AI-driven diagnostics** in Neurodiagnoses.
97
98 For further information, refer to **[[Clinica.Run Documentation>>url:https://clinica.run/]]**.
99
100 ==== ====
101
102 ==== **Data Sources** ====
103
104 [[List of potential sources of databases>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
105
106 **Biomedical Ontologies & Databases:**
107
108 * **Human Phenotype Ontology (HPO)** for symptom annotation.
109 * **Gene Ontology (GO)** for molecular and cellular processes.
110
111 **Dimensionality Reduction and Interpretability:**
112
113 * **Evaluate interpretability** using metrics like the **Area Under the Interpretability Curve (AUIC)**.
114 * **Leverage [[DEIBO>>https://github.com/Mellandd/DEIBO]] (Data-driven Embedding Interpretation Based on Ontologies)** to connect model dimensions to ontology concepts.
115
116 **Neuroimaging & EEG/MEG Data:**
117
118 * **MRI volumetric measures** for brain atrophy tracking.
119 * **EEG functional connectivity patterns** (AI-Mind).
120
121 **Clinical & Biomarker Data:**
122
123 * **CSF biomarkers** (Amyloid-beta, Tau, Neurofilament Light).
124 * **Sleep monitoring and actigraphy data** (ADIS).
125
126 **Federated Learning Integration:**
127
128 * **Secure multi-center data harmonization** (PROMINENT).
129
130 ----
131
132 ==== **Annotation System for Multi-Modal Data** ====
133
134 To ensure **structured integration of diverse datasets**, **Neurodiagnoses** will implement an **AI-driven annotation system**, which will:
135
136 * **Assign standardized metadata tags** to diagnostic features.
137 * **Provide contextual explanations** for AI-based classifications.
138 * **Track temporal disease progression annotations** to identify long-term trends.
139
140 ----
141
142 == **2. AI-Based Analysis** ==
143
144 ==== **Machine Learning & Deep Learning Models** ====
145
146 **Risk Prediction Models:**
147
148 * **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
149
150 **Biomarker Classification & Probabilistic Imputation:**
151
152 * **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
153
154 **Neuroimaging Feature Extraction:**
155
156 * **MRI & EEG data** annotated with **neuroanatomical feature labels**.
157
158 ==== **AI-Powered Annotation System** ====
159
160 * Uses **SHAP-based interpretability tools** to explain model decisions.
161 * Generates **automated clinical annotations** in structured reports.
162 * Links findings to **standardized medical ontologies** (e.g., **SNOMED, HPO**).
163
164 ----
165
166 == **3. Diagnostic Framework & Clinical Decision Support** ==
167
168 ==== **Tridimensional Diagnostic Axes** ====
169
170 **Axis 1: Etiology (Pathogenic Mechanisms)**
171
172 * Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
173 * **AI-assisted annotation** provides **causal interpretations** for clinical use.
174
175 **Axis 2: Molecular Markers & Biomarkers**
176
177 * **Integration of CSF, blood, and neuroimaging biomarkers**.
178 * **Structured annotation** highlights **biological pathways linked to diagnosis**.
179
180 **Axis 3: Neuroanatomoclinical Correlations**
181
182 * **MRI and EEG data** provide anatomical and functional insights.
183 * **AI-generated progression maps** annotate **brain structure-function relationships**.
184
185 ----
186
187 == **4. Computational Workflow & Annotation Pipelines** ==
188
189 ==== **Data Processing Steps** ====
190
191 **Data Ingestion:**
192
193 * **Harmonized datasets** stored in **EBRAINS Bucket**.
194 * **Preprocessing pipelines** clean and standardize data.
195
196 **Feature Engineering:**
197
198 * **AI models** extract **clinically relevant patterns** from **EEG, MRI, and biomarkers**.
199
200 **AI-Generated Annotations:**
201
202 * **Automated tagging** of diagnostic features in **structured reports**.
203 * **Explainability modules (SHAP, LIME)** ensure transparency in predictions.
204
205 **Clinical Decision Support Integration:**
206
207 * **AI-annotated findings** fed into **interactive dashboards**.
208 * **Clinicians can adjust, validate, and modify annotations**.
209
210 ----
211
212 == **5. Validation & Real-World Testing** ==
213
214 ==== **Prospective Clinical Study** ====
215
216 * **Multi-center validation** of AI-based **annotations & risk stratifications**.
217 * **Benchmarking against clinician-based diagnoses**.
218 * **Real-world testing** of AI-powered **structured reporting**.
219
220 ==== **Quality Assurance & Explainability** ====
221
222 * **Annotations linked to structured knowledge graphs** for improved transparency.
223 * **Interactive annotation editor** allows clinicians to validate AI outputs.
224
225 ----
226
227 == **6. Collaborative Development** ==
228
229 The project is **open to contributions** from **researchers, clinicians, and developers**.
230
231 **Key tools include:**
232
233 * **Jupyter Notebooks**: For data analysis and pipeline development.
234 ** Example: **probabilistic imputation**
235 * **Wiki Pages**: For documenting methods and results.
236 * **Drive and Bucket**: For sharing code, data, and outputs.
237 * **Collaboration with related projects**:
238 ** Example: **Beyond the hype: AI in dementia – from early risk detection to disease treatment**
239
240 ----
241
242 == **7. Tools and Technologies** ==
243
244 ==== **Programming Languages:** ====
245
246 * **Python** for AI and data processing.
247
248 ==== **Frameworks:** ====
249
250 * **TensorFlow** and **PyTorch** for machine learning.
251 * **Flask** or **FastAPI** for backend services.
252
253 ==== **Visualization:** ====
254
255 * **Plotly** and **Matplotlib** for interactive and static visualizations.
256
257 ==== **EBRAINS Services:** ====
258
259 * **Collaboratory Lab** for running Notebooks.
260 * **Buckets** for storing large datasets.
261
262 ----
263
264 === **Why This Matters** ===
265
266 * The annotation system ensures that AI-generated insights are structured, interpretable, and clinically meaningful.
267 * It enables real-time tracking of disease progression across the three diagnostic axes.
268 * It facilitates integration with electronic health records and decision-support tools, improving AI adoption in clinical workflows.