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5		-= //A new tridimensional diagnostic framework for complex CNS diseases// =
	5	+= //A new tridimensional diagnostic framework for CNS conditions// =
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 neurological 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.
9	9	)))
10	10	)))
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15	15	(((
16	16	= What is this about and what can I find here? =
17	17
18		-= **Overview** =
	18	+== **Overview** ==
19	19
20		-The classification and diagnosis of central nervous system (CNS) diseases have long been constrained by traditional, phenotype-based approaches that often fail to capture the complex interplay of pathophysiological mechanisms, molecular biomarkers, and neuroanatomical changes.
	20	+The //Tridimensional Diagnostic Framework// redefines how neurodegenerative diseases (NDDs) are classified by focusing on:
21	21
22		-**Neurodiagnoses** redefines this landscape by integrating advanced AI with multi-modal data—including genetics, neuroimaging, biomarkers, and digital health records—to create a more precise, scalable, and data-driven diagnostic system.
	22	+* **Axis 1**: Etiology (genetic/sporadic and environmental factors).
	23	+* **Axis 2**: Molecular Markers (biomarkers and proteinopathies).
	24	+* **Axis 3**: Neuroanatomoclinical correlations (linking clinical symptoms to structural changes in the nervous system).
23	23
24		-Additionally, **Neurodiagnoses is now expanding into disease prediction and biomarker estimation**, integrating state-of-the-art machine learning models to enhance precision diagnostics and disease progression forecasting.
	26	+This methodology enables:
25	25
26		-On this page, you will find:
	28	+* Greater precision in diagnosis.
	29	+* Integration of incomplete datasets using AI-driven probabilistic modeling.
	30	+* Stratification of patients for personalized treatment.
27	27
28		-* Detailed descriptions of both the clinical diagnostic tools and the research framework.
29		-* Access to our AI models, data processing pipelines, and digital twin simulations.
30		-* Collaborative resources for researchers, clinicians, and AI developers.
31		-* Guidelines and instructions on how to contribute to and expand the project.
	32	+== **Diagnostic Axes** ==
32	32
33		-== **The role of AI-powered annotation** ==
34		-
35		-To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
36		-
37		-* Assigns structured metadata tags to diagnostic features.
38		-* Provides real-time contextual explanations for AI-based classifications.
39		-* Tracks longitudinal disease progression using timestamped AI annotations.
40		-* Improves AI model transparency through interpretability tools (e.g., SHAP analysis).
41		-* Facilitates decision-making for clinicians by linking annotations to standardized biomedical ontologies (SNOMED, HPO).
42		-
43		-Neurodiagnoses provides **two complementary AI-driven diagnostic approaches**:
44		-
45		-1. **Probabilistic Diagnosis**
46		- * AI assigns probability scores to multiple possible diagnoses based on biomarker, imaging, and clinical data.
47		- * Useful for differential diagnosis and treatment decision-making.
48		-
49		-2. **Tridimensional Diagnosis**
50		- * Diagnoses are structured based on:
51		- - **(1) Etiology** (genetic, autoimmune, metabolic, infectious).
52		- - **(2) Molecular Biomarkers** (amyloid-beta, tau, inflammatory markers, EEG patterns).
53		- - **(3) Neuroanatomoclinical Correlations** (brain atrophy, connectivity alterations).
54		- * This approach enables precise disease subtyping and biologically meaningful classification, particularly useful for tracking progression over time.
55		-
56		-Both systems will be offered for every patient case, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
57		-
58		-== **Disease Prediction and Biomarker Estimation** ==
59		-
60		-Neurodiagnoses is also implementing **biomarker prediction and disease progression modeling**, using advanced machine learning techniques:
61		-
62		-* **Biomarker Prediction:**
63		- - Estimation of fluid-based and neuroimaging biomarkers without invasive testing.
64		- - Multi-modal machine learning models for predicting molecular and clinical markers.
65		-
66		-* **Disease Progression Modeling:**
67		- - AI-driven forecasts for neurodegenerative disease evolution.
68		- - Probabilistic disease conversion models (e.g., MCI to AD, Parkinson's prodromal phases).
69		- - Survival models and risk stratification for precision medicine applications.
70		-
71		-== **The case of neurodegenerative diseases** ==
72		-
73		-There have been described these 3 diagnostic axes:
74		-
75		-[[Neurodegenerative diseases can be studied and classified in a tridimensional scheme with three axes: anatomic–clinical, molecular, and etiologic. CSF, cerebrospinal fluid; FDG, fluorodeoxyglucose; MRI, magnetic resonance imaging; PET, positron emission tomography.>>image:tridimensional.png||alt="tridimensional view of neurodegenerative diseases"]]
76		-
77	77	* (((
78	78	**Axis 1: Etiology**
	36	+
79	79	* //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
80	80	* //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
81		-* //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
	39	+* //Tests//: Genetic testing, lifestyle and cardiovascular screening.
82	82	)))
83	83	* (((
84	84	**Axis 2: Molecular Markers**
	43	+
85	85	* //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
86	86	* //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
87	87	* //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
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88	88	)))
89	89	* (((
90	90	**Axis 3: Neuroanatomoclinical**
	50	+
91	91	* //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments.
92	92	* //Examples//: Hippocampal atrophy correlating with memory deficits.
93	93	* //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations.
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97	97
98	98	This system enhances:
99	99
100		-* **Research**: By stratifying patients, reducing cohort heterogeneity in clinical trials.
	60	+* **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials.
101	101	* **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
102	102
	63	+== Who has access? ==
	64	+
	65	+We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
	66	+
103	103	== How to Contribute ==
104	104
105	105	* Access the `/docs` folder for guidelines.
106	106	* Use `/code` for the latest AI pipelines.
107	107	* Share feedback and ideas in the wiki discussion pages.
108		-* Join our [[Community on EBRAINS>>https://community.ebrains.eu/_ideas/-OJHTZrpKrrrkx-u0djj/about]]
109		-* Join the [[Discussion Forum at GitHub>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]]
110	110
111	111	== Key Objectives ==
112	112
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113	113	* Develop interpretable AI models for diagnosis and progression tracking.
114	114	* Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
115	115	* Foster collaboration among neuroscientists, AI researchers, and clinicians.
116		-* Provide a dual diagnostic system:
117		- ** Probabilistic Diagnosis – AI assigns multiple traditional possible diagnoses with probability percentages.
118		- ** Tridimensional Diagnosis – AI structures diagnoses based on etiology, biomarkers, and neuroanatomical correlations.
119		-* Implement disease prediction models for neurodegenerative conditions.
120		-* Predict biomarkers from non-invasive data sources.
121		-
122		-== Who has access? ==
123		-
124		-We welcome contributions from the global community. Join us as we transform CNS diagnostics and drive precision medicine forward through a collaborative, open-source approach. Let’s build the future of neurological diagnostics together!
125	125	)))
126	126
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128	128	(((
129	129	{{box title="**Contents**"}}
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136	136	* `/code`: Machine learning pipelines and scripts.
137	137	* `/data`: Sample datasets for testing.
138	138	* `/outputs`: Generated models, visualizations, and reports.
139		-* [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]]
140		-* [[Notebooks>>Notebooks]]
141		-* [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]]
142		-* [[to-do-list>>to-do-list]]
143	143	)))
144	144	)))
145		-

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