Summary

• Page properties (1 modified, 0 added, 0 removed)

Details

Page properties

Content

...	...	@@ -1,109 +1,268 @@
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/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	+
5	5	----
6	6
7	7	=== **1. Data Integration** ===
8	8
	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	+
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.
	104	+[[List of potential sources of databases>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/blob/main/data/sources/list_of_potential_databases]]
18	18
	106	+**Biomedical Ontologies & Databases:**
19	19
20		-==== **Data Preprocessing** ====
	108	+* **Human Phenotype Ontology (HPO)** for symptom annotation.
	109	+* **Gene Ontology (GO)** for molecular and cellular processes.
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.
	111	+**Dimensionality Reduction and Interpretability:**
25	25
	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	+
26	26	----
27	27
	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	+
28	28	=== **2. AI-Based Analysis** ===
29	29
30		-==== **Model Development** ====
	144	+==== **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.
	146	+**Risk Prediction Models:**
36	36
37		-==== **Dimensionality Reduction and Interpretability** ====
	148	+* **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).
	150	+**Biomarker Classification & Probabilistic Imputation:**
41	41
	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	+
42	42	----
43	43
44		-=== **3. Diagnostic Framework** ===
	166	+=== **3. Diagnostic Framework & Clinical Decision Support** ===
45	45
46		-==== **Axes of Diagnosis** ====
	168	+==== **Tridimensional Diagnostic Axes** ====
47	47
48		-The framework organizes diagnostic data into three axes:
	170	+**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).
	172	+* Classification based on **genetic markers, cellular pathways, and environmental risk factors**.
	173	+* **AI-assisted annotation** provides **causal interpretations** for clinical use.
53	53
54		-==== **Recommendation System** ====
	175	+**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.
	177	+* **Integration of CSF, blood, and neuroimaging biomarkers**.
	178	+* **Structured annotation** highlights **biological pathways linked to diagnosis**.
58	58
	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	+
59	59	----
60	60
61		-=== **4. Computational Workflow** ===
	187	+=== **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.
	189	+==== **Data Processing Steps** ====
71	71
	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	+
72	72	----
73	73
74		-=== **5. Validation** ===
	212	+=== **5. Validation & Real-World Testing** ===
75	75
76		-==== **Internal Validation** ====
	214	+==== **Prospective Clinical Study** ====
77	77
78		-* Test the system using simulated datasets and known clinical cases.
79		-* Fine-tune models based on validation results.
	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**.
80	80
81		-==== **External Validation** ====
	220	+==== **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.
	222	+* **Annotations linked to structured knowledge graphs** for improved transparency.
	223	+* **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:
	229	+The project is **open to contributions** from **researchers, clinicians, and developers**.
91	91
	231	+**Key tools include:**
	232	+
92	92	* **Jupyter Notebooks**: For data analysis and pipeline development.
93		-** Example: [[probabilistic imputation>>https://drive.ebrains.eu/f/4f69ab52f7734ef48217/]]
	234	+** 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/]]
	237	+* **Collaboration with related projects**:
	238	+** 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.
	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.