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17	17
18	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	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	+**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	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	+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.
24	24
25		-The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
	26	+On this page, you will find:
26	26
27		-* **Axis 1**: Etiology (genetic or other causes of diseases).
28		-* **Axis 2**: Molecular Markers (biomarkers).
29		-* **Axis 3**: Neuroanatomoclinical correlations (linking clinical symptoms to structural changes in the nervous system).
	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.
30	30
31		-This methodology enables:
32		-
33		-* Greater precision in diagnosis.
34		-* Integration of incomplete datasets using AI-driven probabilistic modeling.
35		-* Stratification of patients for personalized treatment.
36		-
37	37	== **The role of AI-powered annotation** ==
38	38
39	39	To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
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44	44	* Improves AI model transparency through interpretability tools (e.g., SHAP analysis).
45	45	* Facilitates decision-making for clinicians by linking annotations to standardized biomedical ontologies (SNOMED, HPO).
46	46
47		-Neurodiagnoses provides two complementary AI-driven diagnostic approaches:
	43	+Neurodiagnoses provides **two complementary AI-driven diagnostic approaches**:
48	48
49		-1. Traditional Probabilistic Diagnosis
	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.
50	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.
	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.
57	57
58		-2. Tridimensional Diagnosis
	56	+Both systems will be offered for every patient case, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
59	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.
	58	+== **Disease Prediction and Biomarker Estimation** ==
65	65
66		-For every patient case, both systems will be offered, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
	60	+Neurodiagnoses is also implementing **biomarker prediction and disease progression modeling**, using advanced machine learning techniques:
67	67
	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.
68	68
	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	+
69	69	== **The case of neurodegenerative diseases** ==
70	70
71	71	There have been described these 3 diagnostic axes:
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74	74
75	75	* (((
76	76	**Axis 1: Etiology**
77		-
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**
86		-
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**
95		-
96	96	* //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments.
97	97	* //Examples//: Hippocampal atrophy correlating with memory deficits.
98	98	* //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations.
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102	102
103	103	This system enhances:
104	104
105		-* **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials.
	100	+* **Research**: By stratifying patients, reducing cohort heterogeneity in clinical trials.
106	106	* **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
107	107
108	108	== How to Contribute ==
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110	110	* Access the `/docs` folder for guidelines.
111	111	* Use `/code` for the latest AI pipelines.
112	112	* 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]]
113	113
114	114	== Key Objectives ==
115	115
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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	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.
	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.
122	122
123	123	== Who has access? ==
124	124
125		-We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
	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!
126	126	)))
127	127
128		-
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130		-
131		-
132		-
133	133	(% class="col-xs-12 col-sm-4" %)
134	134	(((
135	135	{{box title="**Contents**"}}
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148	148	* [[to-do-list>>to-do-list]]
149	149	)))
150	150	)))
	145	+