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1		-=== **Overview** ===
	1	+==== **Overview** ====
2	2
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.
	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	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	+
5	5	----
6	6
7	7	=== **1. Data Integration** ===
...	...	@@ -8,102 +8,166 @@
8	8
9	9	==== **Data Sources** ====
10	10
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.
	27	+**Biomedical Ontologies & Databases:**
18	18
	29	+* **Human Phenotype Ontology (HPO)** for symptom annotation.
	30	+* **Gene Ontology (GO)** for molecular and cellular processes.
19	19
20		-==== **Data Preprocessing** ====
	32	+**Dimensionality Reduction and Interpretability:**
21	21
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.
	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.
25	25
	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	+
26	26	----
27	27
	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	+
28	28	=== **2. AI-Based Analysis** ===
29	29
30		-==== **Model Development** ====
	65	+==== **Machine Learning & Deep Learning Models** ====
31	31
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.
	67	+**Risk Prediction Models:**
36	36
37		-==== **Dimensionality Reduction and Interpretability** ====
	69	+* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
38	38
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).
	71	+**Biomarker Classification & Probabilistic Imputation:**
41	41
	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	+
42	42	----
43	43
44		-=== **3. Diagnostic Framework** ===
	87	+=== **3. Diagnostic Framework & Clinical Decision Support** ===
45	45
46		-==== **Axes of Diagnosis** ====
	89	+==== **Tridimensional Diagnostic Axes** ====
47	47
48		-The framework organizes diagnostic data into three axes:
	91	+**Axis 1: Etiology (Pathogenic Mechanisms)**
49	49
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).
	93	+* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
	94	+* **AI-assisted annotation** provides **causal interpretations** for clinical use.
53	53
54		-==== **Recommendation System** ====
	96	+**Axis 2: Molecular Markers & Biomarkers**
55	55
56		-* Suggests additional tests or biomarkers if gaps are detected in the data.
57		-* Prioritizes tests based on clinical impact and cost-effectiveness.
	98	+* **Integration of CSF, blood, and neuroimaging biomarkers**.
	99	+* **Structured annotation** highlights **biological pathways linked to diagnosis**.
58	58
	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	+
59	59	----
60	60
61		-=== **4. Computational Workflow** ===
	108	+=== **4. Computational Workflow & Annotation Pipelines** ===
62	62
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.
	110	+==== **Data Processing Steps** ====
71	71
	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	+
72	72	----
73	73
74		-=== **5. Validation** ===
	133	+=== **5. Validation & Real-World Testing** ===
75	75
76		-==== **Internal Validation** ====
	135	+==== **Prospective Clinical Study** ====
77	77
78		-* Test the system using simulated datasets and known clinical cases.
79		-* Fine-tune models based on validation results.
	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**.
80	80
81		-==== **External Validation** ====
	141	+==== **Quality Assurance & Explainability** ====
82	82
83		-* Collaborate with research institutions and hospitals to test the system in real-world settings.
84		-* Use anonymized patient data to ensure privacy compliance.
	143	+* **Annotations linked to structured knowledge graphs** for improved transparency.
	144	+* **Interactive annotation editor** allows clinicians to validate AI outputs.
85	85
86	86	----
87	87
88	88	=== **6. Collaborative Development** ===
89	89
90		-The project is open to contributions from researchers, clinicians, and developers. Key tools include:
	150	+The project is **open to contributions** from **researchers, clinicians, and developers**.
91	91
	152	+**Key tools include:**
	153	+
92	92	* **Jupyter Notebooks**: For data analysis and pipeline development.
93		-** Example: [[probabilistic imputation>>https://drive.ebrains.eu/f/4f69ab52f7734ef48217/]]
	155	+** Example: **probabilistic imputation**
94	94	* **Wiki Pages**: For documenting methods and results.
95	95	* **Drive and Bucket**: For sharing code, data, and outputs.
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/]]
	158	+* **Collaboration with related projects**:
	159	+** Example: **Beyond the hype: AI in dementia – from early risk detection to disease treatment**
97	97
98	98	----
99	99
100	100	=== **7. Tools and Technologies** ===
101	101
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.
	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.**