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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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10	10	)))
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18	18	= **Overview** =
19	19
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.
	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. Neurodegenerative and psychiatric disorders, for example, exhibit significant clinical overlap, co-pathology, and heterogeneity, making current diagnostic models insufficient.
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.
	23	+This project proposes a new diagnostic framework—one that shifts from symptom-based classifications to an etiology-driven, tridimensional system. By integrating genetics, proteomics, neuroimaging, computational modeling, and AI-powered annotations, this approach aims to provide a more precise, scalable, and biologically grounded method for diagnosing and managing CNS diseases.
24	24
	25	+The AI-powered annotation system plays a critical role by structuring, interpreting, and tracking multi-modal data, ensuring real-time disease progression analysis, clinician decision support, and personalized treatment pathways.
	26	+
	27	+=== **Project Aim** ===
	28	+
	29	+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.
	30	+
25	25	The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
26	26
27	27	* **Axis 1**: Etiology (genetic or other causes of diseases).
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34	34	* Integration of incomplete datasets using AI-driven probabilistic modeling.
35	35	* Stratification of patients for personalized treatment.
36	36
37		-== **The role of AI-powered annotation** ==
	43	+== **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:
40	40
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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:
48		-
49		-1. Traditional Probabilistic Diagnosis
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.
57		-
58		-2. Tridimensional Diagnosis
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.
65		-
66		-For every patient case, both systems will be offered, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
67		-
68		-
69	69	== **The case of neurodegenerative diseases** ==
70	70
71	71	There have been described these 3 diagnostic axes:
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116	116	* Develop interpretable AI models for diagnosis and progression tracking.
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		-* 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.
122	122
123	123	== Who has access? ==
124	124