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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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16	16	= What is this about and what can I find here? =
17	17
18		-== **Overview** ==
	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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23	23	* **Axis 2**: Molecular Markers (biomarkers).
24	24	* **Axis 3**: Neuroanatomoclinical correlations (linking clinical symptoms to structural changes in the nervous system).
25	25
26		-
27	27	This methodology enables:
28	28
29	29	* Greater precision in diagnosis.
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30	30	* Integration of incomplete datasets using AI-driven probabilistic modeling.
31	31	* Stratification of patients for personalized treatment.
32	32
	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	+
33	33	== **The case of neurodegenerative diseases** ==
34	34
35	35	There have been described these 3 diagnostic axes:
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42	42	* //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
43	43	* //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
44	44	* //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
	81	+
	82	+
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46	46	* (((
47	47	**Axis 2: Molecular Markers**
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49	49	* //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
50	50	* //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
51	51	* //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
	90	+
	91	+
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53	53	* (((
54	54	**Axis 3: Neuroanatomoclinical**
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65	65	* **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials.
66	66	* **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
67	67
68		-== Who has access? ==
69		-
70		-We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
71		-
72	72	== How to Contribute ==
73	73
74	74	* Access the `/docs` folder for guidelines.
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80	80	* Develop interpretable AI models for diagnosis and progression tracking.
81	81	* Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
82	82	* 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	+
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88	88	{{box title="**Contents**"}}
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96	96	* `/data`: Sample datasets for testing.
97	97	* `/outputs`: Generated models, visualizations, and reports.
98	98	* [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]]
	143	+* [[Notebooks>>Notebooks]]
99	99	* [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]]
100	100	* [[to-do-list>>to-do-list]]
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