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18	18	= **Overview** =
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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.
	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.
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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.
	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.
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25		-On this page, you will find:
	25	+The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
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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.
	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).
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	31	+This methodology enables:
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	33	+* Greater precision in diagnosis.
	34	+* Integration of incomplete datasets using AI-driven probabilistic modeling.
	35	+* Stratification of patients for personalized treatment.
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33	33	== **The role of AI-powered annotation** ==
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35	35	To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
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37		-* Assign structured metadata tags to diagnostic features.
	41	+* Assigns 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).
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45	45	1. Traditional Probabilistic Diagnosis
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47	47	* 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
48	48	* Useful for differential diagnosis and treatment decision-making.
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50	50	2. Tridimensional Diagnosis
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53	53	(1) Etiology (genetic, autoimmune, metabolic, infectious)
54	54	(2) Molecular Biomarkers (amyloid-beta, tau, inflammatory markers, EEG patterns)
55	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.
	64	+* This approach enables precise disease subtyping and biologically meaningful classification, particularly useful to track progression over time.
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58		-Both systems will be offered for every patient case, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
	66	+For every patient case, both systems will be offered, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
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61	61	== **The case of neurodegenerative diseases** ==
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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.
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115	115	== Who has access? ==
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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!
	122	+We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
118	118	)))
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