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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, 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**, 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.
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	+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**.
25	25
26		-On this page, you will find:
	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**.
27	27
28		-* Detailed descriptions of both the clinical diagnostic tools and the research framework.
29		-* Access to our AI models, data processing pipelines, and digital twin simulations.
30		-* Collaborative resources for researchers, clinicians, and AI developers.
31		-* Guidelines and instructions on how to contribute to and expand the project.
	27	+=== **Project Aim** ===
32	32
33		-== **The role of AI-powered annotation** ==
	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**.
34	34
35		-To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
	31	+The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
36	36
37		-* Assigns 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).
	33	+* **Axis 1**: Etiology (genetic or other causes of diseases).
	34	+* **Axis 2**: Molecular Markers (biomarkers).
	35	+* **Axis 3**: Neuroanatomoclinical correlations (linking clinical symptoms to structural changes in the nervous system).
42	42
43		-Neurodiagnoses provides **two complementary AI-driven diagnostic approaches**:
	37	+This methodology enables:
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.
	39	+* Greater precision in diagnosis.
	40	+* Integration of incomplete datasets using AI-driven probabilistic modeling.
	41	+* Stratification of patients for personalized treatment.
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.
	43	+== **The Role of AI-Powered Annotation** ==
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.
	45	+To enhance **standardization, interpretability, and clinical application**, the framework integrates **an AI-powered annotation system**, which:
57	57
58		-== **Disease Prediction and Biomarker Estimation** ==
	47	+* **Assigns structured metadata tags** to diagnostic features.
	48	+* **Provides real-time contextual explanations** for AI-based classifications.
	49	+* **Tracks longitudinal disease progression** using timestamped AI annotations.
	50	+* **Improves AI model transparency** through interpretability tools (e.g., SHAP analysis).
	51	+* **Facilitates decision-making for clinicians** by linking annotations to standardized biomedical ontologies (SNOMED, HPO).
59	59
60		-Neurodiagnoses is also implementing **biomarker prediction and disease progression modeling**, using advanced machine learning techniques:
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		-
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**
	61	+
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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83	83	* (((
84	84	**Axis 2: Molecular Markers**
	68	+
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).
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88	88	)))
89	89	* (((
90	90	**Axis 3: Neuroanatomoclinical**
	75	+
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.
	85	+* **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		-* Join the [[Discussion Forum at GitHub>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]]
110	110
111	111	== Key Objectives ==
112	112
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113	113	* Develop interpretable AI models for diagnosis and progression tracking.
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		-* 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.
121	121
122	122	== Who has access? ==
123	123
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!
	102	+We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
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129	129	{{box title="**Contents**"}}
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142	142	* [[to-do-list>>to-do-list]]
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