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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	5	----
6	6
...	...	@@ -8,166 +8,102 @@
8	8
9	9	==== **Data Sources** ====
10	10
11		-**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.
12	12
13		-* **Human Phenotype Ontology (HPO)** for symptom annotation.
14		-* **Gene Ontology (GO)** for molecular and cellular processes.
15	15
16		-**Dimensionality Reduction and Interpretability:**
	20	+==== **Data Preprocessing** ====
17	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.
	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.
20	20
21		-**Neuroimaging & EEG/MEG Data:**
22		-
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).
34		-
35	35	----
36	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:
40		-
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.
44		-
45		-----
46		-
47	47	=== **2. AI-Based Analysis** ===
48	48
49		-==== **Machine Learning & Deep Learning Models** ====
	30	+==== **Model Development** ====
50	50
51		-**Risk Prediction Models:**
	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.
52	52
53		-* **LETHE’s cognitive risk prediction model** integrated into the annotation framework.
	37	+==== **Dimensionality Reduction and Interpretability** ====
54	54
55		-**Biomarker Classification & Probabilistic Imputation:**
	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).
56	56
57		-* **KNN Imputer** and **Bayesian models** used for handling **missing biomarker data**.
58		-
59		-**Neuroimaging Feature Extraction:**
60		-
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**).
68		-
69	69	----
70	70
71		-=== **3. Diagnostic Framework & Clinical Decision Support** ===
	44	+=== **3. Diagnostic Framework** ===
72	72
73		-==== **Tridimensional Diagnostic Axes** ====
	46	+==== **Axes of Diagnosis** ====
74	74
75		-**Axis 1: Etiology (Pathogenic Mechanisms)**
	48	+The framework organizes diagnostic data into three axes:
76	76
77		-* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
78		-* **AI-assisted annotation** provides **causal interpretations** for clinical use.
	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).
79	79
80		-**Axis 2: Molecular Markers & Biomarkers**
	54	+==== **Recommendation System** ====
81	81
82		-* **Integration of CSF, blood, and neuroimaging biomarkers**.
83		-* **Structured annotation** highlights **biological pathways linked to diagnosis**.
	56	+* Suggests additional tests or biomarkers if gaps are detected in the data.
	57	+* Prioritizes tests based on clinical impact and cost-effectiveness.
84	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**.
89		-
90	90	----
91	91
92		-=== **4. Computational Workflow & Annotation Pipelines** ===
	61	+=== **4. Computational Workflow** ===
93	93
94		-==== **Data Processing Steps** ====
	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.
95	95
96		-**Data Ingestion:**
97		-
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:**
106		-
107		-* **Automated tagging** of diagnostic features in **structured reports**.
108		-* **Explainability modules (SHAP, LIME)** ensure transparency in predictions.
109		-
110		-**Clinical Decision Support Integration:**
111		-
112		-* **AI-annotated findings** fed into **interactive dashboards**.
113		-* **Clinicians can adjust, validate, and modify annotations**.
114		-
115	115	----
116	116
117		-=== **5. Validation & Real-World Testing** ===
	74	+=== **5. Validation** ===
118	118
119		-==== **Prospective Clinical Study** ====
	76	+==== **Internal Validation** ====
120	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**.
	78	+* Test the system using simulated datasets and known clinical cases.
	79	+* Fine-tune models based on validation results.
124	124
125		-==== **Quality Assurance & Explainability** ====
	81	+==== **External Validation** ====
126	126
127		-* **Annotations linked to structured knowledge graphs** for improved transparency.
128		-* **Interactive annotation editor** allows clinicians to validate AI outputs.
	83	+* Collaborate with research institutions and hospitals to test the system in real-world settings.
	84	+* Use anonymized patient data to ensure privacy compliance.
129	129
130	130	----
131	131
132	132	=== **6. Collaborative Development** ===
133	133
134		-The project is **open to contributions** from **researchers, clinicians, and developers**.
	90	+The project is open to contributions from researchers, clinicians, and developers. Key tools include:
135	135
136		-**Key tools include:**
137		-
138	138	* **Jupyter Notebooks**: For data analysis and pipeline development.
139		-** Example: **probabilistic imputation**
	93	+** Example: [[probabilistic imputation>>https://drive.ebrains.eu/f/4f69ab52f7734ef48217/]]
140	140	* **Wiki Pages**: For documenting methods and results.
141	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**
	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/]]
144	144
145	145	----
146	146
147	147	=== **7. Tools and Technologies** ===
148	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.**
	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.