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5		-= //A new tridimensional diagnostic framework for complex CNS diseases// =
	5	+= //A new tridimensional diagnostic framework for CNS diseases// =
6	6
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.
	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.
8	8	We aim to create a structured, interpretable, and scalable diagnostic tool.
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10	10	)))
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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		-
25	25	The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
26	26
27	27	* **Axis 1**: Etiology (genetic or other causes of diseases).
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34	34	* Integration of incomplete datasets using AI-driven probabilistic modeling.
35	35	* Stratification of patients for personalized treatment.
36	36
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		-
69	69	== **The case of neurodegenerative diseases** ==
70	70
71	71	There have been described these 3 diagnostic axes:
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78	78	* //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
79	79	* //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
80	80	* //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
81		-
82		-
83	83	)))
84	84	* (((
85	85	**Axis 2: Molecular Markers**
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87	87	* //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
88	88	* //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
89	89	* //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
90		-
91		-
92	92	)))
93	93	* (((
94	94	**Axis 3: Neuroanatomoclinical**
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105	105	* **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials.
106	106	* **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
107	107
	67	+== Who has access? ==
	68	+
	69	+We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
	70	+
108	108	== How to Contribute ==
109	109
110	110	* Access the `/docs` folder for guidelines.
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116	116	* Develop interpretable AI models for diagnosis and progression tracking.
117	117	* Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
118	118	* 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!
123	123	)))
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125	125
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128	128
129		-
130	130	(% class="col-xs-12 col-sm-4" %)
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132	132	{{box title="**Contents**"}}
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140	140	* `/data`: Sample datasets for testing.
141	141	* `/outputs`: Generated models, visualizations, and reports.
142	142	* [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]]
143		-* [[Notebooks>>Notebooks]]
144	144	* [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]]
145	145	* [[to-do-list>>to-do-list]]
146	146	)))