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18	18	= **Overview** =
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	22	+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.
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	24	+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**.
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	26	+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**.
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	28	+=== **Project Aim** ===
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	30	+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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20	20	The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
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22	22	* **Axis 1**: Etiology (genetic or other causes of diseases).
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29	29	* Integration of incomplete datasets using AI-driven probabilistic modeling.
30	30	* Stratification of patients for personalized treatment.
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	44	+== **The Role of AI-Powered Annotation** ==
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	46	+To enhance **standardization, interpretability, and clinical application**, the framework integrates **an AI-powered annotation system**, which:
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	48	+* **Assigns structured metadata tags** to diagnostic features.
	49	+* **Provides real-time contextual explanations** for AI-based classifications.
	50	+* **Tracks longitudinal disease progression** using timestamped AI annotations.
	51	+* **Improves AI model transparency** through interpretability tools (e.g., SHAP analysis).
	52	+* **Facilitates decision-making for clinicians** by linking annotations to standardized biomedical ontologies (SNOMED, HPO).
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32	32	== **The case of neurodegenerative diseases** ==
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34	34	There have been described these 3 diagnostic axes:
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64	64	* **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials.
65	65	* **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
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67		-== Who has access? ==
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69		-We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
70		-
71	71	== How to Contribute ==
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73	73	* Access the `/docs` folder for guidelines.
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79	79	* Develop interpretable AI models for diagnosis and progression tracking.
80	80	* Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
81	81	* Foster collaboration among neuroscientists, AI researchers, and clinicians.
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	101	+== Who has access? ==
	102	+
	103	+We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
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88	88	(% class="col-xs-12 col-sm-4" %)
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90	90	{{box title="**Contents**"}}