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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.
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.
	21	+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. 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.
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.
	23	+In addition to these clinical diagnostic approaches, Neurodiagnoses has expanded into a research-oriented platform through the integration of **CNS Digital Twins**. This cutting-edge concept involves creating a personalized digital replica of a patient’s CNS by incorporating multi-omics data (proteomics, genomics, lipidomics, transcriptomics), various neuroimaging modalities, and digital health information. These digital twins enable simulations of disease progression, support the discovery of novel biomarkers, and help identify new therapeutic targets.
25	25
26	26	On this page, you will find:
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34	34
35	35	To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
36	36
37		-* Assigns structured metadata tags to diagnostic features.
	36	+* Assign structured metadata tags to diagnostic features.
38	38	* Provides real-time contextual explanations for AI-based classifications.
39	39	* Tracks longitudinal disease progression using timestamped AI annotations.
40	40	* Improves AI model transparency through interpretability tools (e.g., SHAP analysis).
41	41	* Facilitates decision-making for clinicians by linking annotations to standardized biomedical ontologies (SNOMED, HPO).
42	42
43		-Neurodiagnoses provides **two complementary AI-driven diagnostic approaches**:
	42	+Neurodiagnoses provides two complementary AI-driven diagnostic approaches:
44	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.
	44	+1. Traditional Probabilistic Diagnosis
48	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.
	46	+* AI provides multiple possible diagnoses, each assigned a probability percentage based on biomarker, imaging, and clinical data.
	47	+* Useful for differential diagnosis and treatment decision-making.
55	55
56		-Both systems will be offered for every patient case, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
	49	+2. Tridimensional Diagnosis
57	57
58		-== **Disease Prediction and Biomarker Estimation** ==
	51	+* Diagnoses are structured based on:
	52	+(1) Etiology (genetic, autoimmune, metabolic, infectious)
	53	+(2) Molecular Biomarkers (amyloid-beta, tau, inflammatory markers, EEG patterns)
	54	+(3) Neuroanatomoclinical Correlations (brain atrophy, connectivity alterations)
	55	+* This approach enables precise disease subtyping and biologically meaningful classification, particularly useful for tracking progression over time.
59	59
60		-Neurodiagnoses is also implementing **biomarker prediction and disease progression modeling**, using advanced machine learning techniques:
	57	+Both systems will be offered for every patient case, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
61	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	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	71	== **The case of neurodegenerative diseases** ==
72	72
73	73	There have been described these 3 diagnostic axes:
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76	76
77	77	* (((
78	78	**Axis 1: Etiology**
	68	+
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	81	* //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
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	73	+
82	82	)))
83	83	* (((
84	84	**Axis 2: Molecular Markers**
	77	+
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).
	81	+
	82	+
88	88	)))
89	89	* (((
90	90	**Axis 3: Neuroanatomoclinical**
	86	+
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.
	96	+* **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
103	103	== How to Contribute ==
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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	109	* Join the [[Discussion Forum at GitHub>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]]
110	110
111	111	== Key Objectives ==
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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	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.
	112	+** Probabilistic Diagnosis – AI assigns multiple traditional possible diagnoses with probability percentages.
	113	+** Tridimensional Diagnosis – AI structures diagnoses based on etiology, biomarkers, and neuroanatomical correlations.
121	121
122	122	== Who has access? ==
123	123
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124	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!
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	124	+
127	127	(% class="col-xs-12 col-sm-4" %)
128	128	(((
129	129	{{box title="**Contents**"}}
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142	142	* [[to-do-list>>to-do-list]]
143	143	)))
144	144	)))
145		-