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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.