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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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10	10	)))
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17	17
18	18	= **Overview** =
19	19
20		-T
21	21
22		-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. **Neurodegenerative and psychiatric disorders**, for example, exhibit significant **clinical overlap, co-pathology, and heterogeneity**, making current diagnostic models insufficient.
	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.
23	23
24		-This project proposes a **new diagnostic framework**—one that **shifts from symptom-based classifications** to an **etiology-driven, tridimensional system**. By integrating **genetics, proteomics, neuroimaging, computational modeling, and AI-powered annotations**, this approach aims to provide a **more precise, scalable, and biologically grounded method for diagnosing and managing CNS diseases**.
	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.
25	25
26		-The **AI-powered annotation system** plays a critical role by **structuring, interpreting, and tracking multi-modal data**, ensuring **real-time disease progression analysis, clinician decision support, and personalized treatment pathways**.
27		-
28		-=== **Project Aim** ===
29		-
30		-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**.
31		-
32	32	The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
33	33
34	34	* **Axis 1**: Etiology (genetic or other causes of diseases).
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41	41	* Integration of incomplete datasets using AI-driven probabilistic modeling.
42	42	* Stratification of patients for personalized treatment.
43	43
44		-== **The Role of AI-Powered Annotation** ==
	37	+== **The role of AI-powered annotation** ==
45	45
46		-To enhance **standardization, interpretability, and clinical application**, the framework integrates **an AI-powered annotation system**, which:
	39	+To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
47	47
48		-* **Assigns structured metadata tags** to diagnostic features.
49		-* **Provides real-time contextual explanations** for AI-based classifications.
50		-* **Tracks longitudinal disease progression** using timestamped AI annotations.
51		-* **Improves AI model transparency** through interpretability tools (e.g., SHAP analysis).
52		-* **Facilitates decision-making for clinicians** by linking annotations to standardized biomedical ontologies (SNOMED, HPO).
	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).
53	53
	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	+
54	54	== **The case of neurodegenerative diseases** ==
55	55
56	56	There have been described these 3 diagnostic axes:
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63	63	* //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
64	64	* //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
65	65	* //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
	81	+
	82	+
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67	67	* (((
68	68	**Axis 2: Molecular Markers**
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70	70	* //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
71	71	* //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
72	72	* //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
	90	+
	91	+
73	73	)))
74	74	* (((
75	75	**Axis 3: Neuroanatomoclinical**
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97	97	* Develop interpretable AI models for diagnosis and progression tracking.
98	98	* Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
99	99	* Foster collaboration among neuroscientists, AI researchers, and clinicians.
	119	+* Provide a dual diagnostic system:
	120	+** Probabilistic Diagnosis – AI assigns multiple traditional possible diagnoses with probability percentages.
	121	+** Tridimensional Diagnosis – AI structures diagnoses based on etiology, biomarkers, and neuroanatomical correlations.
100	100
101	101	== Who has access? ==
102	102
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121	121	* `/data`: Sample datasets for testing.
122	122	* `/outputs`: Generated models, visualizations, and reports.
123	123	* [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]]
	146	+* [[Notebooks>>Notebooks]]
124	124	* [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]]
125	125	* [[to-do-list>>to-do-list]]
126	126	)))