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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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17	17
18	18	= **Overview** =
19	19
	20	+The classification and diagnosis of central nervous system (CNS) diseases have long been constrained by traditional phenotypic approaches that fail to capture the underlying pathophysiological mechanisms, molecular biomarkers, and neuroanatomical changes that drive disease progression. For instance, neurodegenerative and psychiatric disorders exhibit significant clinical overlap, co-pathology, and heterogeneity, a new diagnostic framework is urgently needed—one that shifts from symptom-based classifications toward an etiology-driven, tridimensional system integrating genetics, proteomics, neuroimaging, and computational modeling. By leveraging AI, multi-modal biomarkers, and precision medicine, this framework aims to provide a more objective, scalable, and biologically grounded approach to diagnosing and managing CNS diseases, ultimately leading to earlier detection, personalized interventions, and improved patient outcomes.
20	20
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.
	22	+The project aims to develop a tridimensional diagnostic framework with an AI-powered annotation system, integrating etiology, molecular biomarkers, and neuroanatomoclinical correlations for precise and scalable CNS disease diagnostics.
22	22
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.
	24	+The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
24	24
25		-On this page, you will find:
	26	+* **Axis 1**: Etiology (genetic or other causes of diseases).
	27	+* **Axis 2**: Molecular Markers (biomarkers).
	28	+* **Axis 3**: Neuroanatomoclinical correlations (linking clinical symptoms to structural changes in the nervous system).
26	26
27		-* Detailed descriptions of both the clinical diagnostic tools and the research framework.
28		-* Access to our AI models, data processing pipelines, and digital twin simulations.
29		-* Collaborative resources for researchers, clinicians, and AI developers.
30		-* Guidelines and instructions on how to contribute to and expand the project.
	30	+This methodology enables:
31	31
	32	+* Greater precision in diagnosis.
	33	+* Integration of incomplete datasets using AI-driven probabilistic modeling.
	34	+* Stratification of patients for personalized treatment.
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		-* Assign 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. Traditional Probabilistic Diagnosis
46		-
47		-* AI provides multiple possible diagnoses, each assigned a probability percentage based on biomarker, imaging, and clinical data.
48		-* Useful for differential diagnosis and treatment decision-making.
49		-
50		-2. Tridimensional Diagnosis
51		-
52		-* Diagnoses are structured based on:
53		-(1) Etiology (genetic, autoimmune, metabolic, infectious)
54		-(2) Molecular Biomarkers (amyloid-beta, tau, inflammatory markers, EEG patterns)
55		-(3) Neuroanatomoclinical Correlations (brain atrophy, connectivity alterations)
56		-* This approach enables precise disease subtyping and biologically meaningful classification, particularly useful for tracking progression over time.
57		-
58		-Both systems will be offered for every patient case, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
59		-
60		-
61	61	== **The case of neurodegenerative diseases** ==
62	62
63	63	There have been described these 3 diagnostic axes:
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70	70	* //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
71	71	* //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
72	72	* //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
73		-
74		-
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76	76	* (((
77	77	**Axis 2: Molecular Markers**
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79	79	* //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
80	80	* //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
81	81	* //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
82		-
83		-
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85	85	* (((
86	86	**Axis 3: Neuroanatomoclinical**
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108	108	* Develop interpretable AI models for diagnosis and progression tracking.
109	109	* Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
110	110	* Foster collaboration among neuroscientists, AI researchers, and clinicians.
111		-* Provide a dual diagnostic system:
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.
114	114
115	115	== Who has access? ==
116	116
117		-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!
	85	+We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
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135	135	* `/data`: Sample datasets for testing.
136	136	* `/outputs`: Generated models, visualizations, and reports.
137	137	* [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]]
138		-* [[Notebooks>>Notebooks]]
139	139	* [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]]
140	140	* [[to-do-list>>to-do-list]]
141	141	)))