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5		-= //A new tridimensional diagnostic framework for CNS diseases// =
	5	+= //A new tridimensional diagnostic framework for complex CNS diseases// =
6	6
7		-This project is focused on developing a novel nosological and diagnostic framework for CNS diseases by using advanced AI techniques and integrating data from neuroimaging, biomarkers, and biomedical ontologies.
	7	+This project is focused on developing a novel nosological and diagnostic framework for complex CNS diseases by using advanced AI techniques and integrating data from neuroimaging, biomarkers, and biomedical ontologies.
8	8	We aim to create a structured, interpretable, and scalable diagnostic tool.
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17	17
18	18	= **Overview** =
19	19
	20	+
	21	+The classification and diagnosis of central nervous system (CNS) diseases have long been constrained by traditional phenotype-based approaches, which often fail to capture the complex pathophysiological mechanisms, molecular biomarkers, and neuroanatomical changes that drive disease progression.
	22	+
	23	+Neurodiagnoses is an open-source AI-powered diagnostic system designed for complex CNS disorders, including neurodegenerative diseases, autoimmune encephalopathies, prion disorders, and genetic syndromes. The project aims to develop a tridimensional diagnostic framework with an AI-powered annotation system, integrating etiology, molecular biomarkers, and neuroanatomoclinical correlations for precise, standardized, and scalable CNS disease diagnostics.
	24	+
20	20	The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
21	21
22	22	* **Axis 1**: Etiology (genetic or other causes of diseases).
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29	29	* Integration of incomplete datasets using AI-driven probabilistic modeling.
30	30	* Stratification of patients for personalized treatment.
31	31
	37	+== **The role of AI-powered annotation** ==
	38	+
	39	+To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
	40	+
	41	+* Assigns structured metadata tags to diagnostic features.
	42	+* Provides real-time contextual explanations for AI-based classifications.
	43	+* Tracks longitudinal disease progression using timestamped AI annotations.
	44	+* Improves AI model transparency through interpretability tools (e.g., SHAP analysis).
	45	+* Facilitates decision-making for clinicians by linking annotations to standardized biomedical ontologies (SNOMED, HPO).
	46	+
	47	+Neurodiagnoses provides two complementary AI-driven diagnostic approaches:
	48	+
	49	+1. Traditional Probabilistic Diagnosis
	50	+
	51	+* AI provides multiple possible diagnoses, each assigned a probability percentage based on biomarker, imaging, and clinical data.
	52	+* Example Output:
	53	+** 75% Alzheimer's Disease
	54	+** 20% Lewy Body Dementia
	55	+** 5% Vascular Dementia
	56	+* Useful for differential diagnosis and treatment decision-making.
	57	+
	58	+2. Tridimensional Diagnosis
	59	+
	60	+* Diagnoses are structured based on:
	61	+(1) Etiology (genetic, autoimmune, metabolic, infectious)
	62	+(2) Molecular Biomarkers (amyloid-beta, tau, inflammatory markers, EEG patterns)
	63	+(3) Neuroanatomoclinical Correlations (brain atrophy, connectivity alterations)
	64	+* This approach enables precise disease subtyping and biologically meaningful classification, particularly useful to track progression over time.
	65	+
	66	+For every patient case, both systems will be offered, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
	67	+
	68	+
32	32	== **The case of neurodegenerative diseases** ==
33	33
34	34	There have been described these 3 diagnostic axes:
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41	41	* //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
42	42	* //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
43	43	* //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
	81	+
	82	+
44	44	)))
45	45	* (((
46	46	**Axis 2: Molecular Markers**
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48	48	* //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
49	49	* //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
50	50	* //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
	90	+
	91	+
51	51	)))
52	52	* (((
53	53	**Axis 3: Neuroanatomoclinical**
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64	64	* **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials.
65	65	* **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
66	66
67		-== Who has access? ==
68		-
69		-We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
70		-
71	71	== How to Contribute ==
72	72
73	73	* Access the `/docs` folder for guidelines.
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79	79	* Develop interpretable AI models for diagnosis and progression tracking.
80	80	* Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
81	81	* Foster collaboration among neuroscientists, AI researchers, and clinicians.
	119	+
	120	+== Who has access? ==
	121	+
	122	+We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
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	129	+
88	88	(% class="col-xs-12 col-sm-4" %)
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90	90	{{box title="**Contents**"}}
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98	98	* `/data`: Sample datasets for testing.
99	99	* `/outputs`: Generated models, visualizations, and reports.
100	100	* [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]]
	143	+* [[Notebooks>>Notebooks]]
101	101	* [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]]
102	102	* [[to-do-list>>to-do-list]]
103	103	)))