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5		-= //A new tridimensional diagnostic framework for CNS diseases// =
	5	+= //A new tridimensional diagnostic framework for complex CNS diseases// =
6	6
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.
	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.
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.
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. Neurodegenerative and psychiatric disorders, for example, exhibit significant clinical overlap, co-pathology, and heterogeneity, making current diagnostic models insufficient.
	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.
22	22
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	+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.
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	+On this page, you will find:
26	26
27		-=== **Project Aim** ===
	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.
28	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.
	33	+== **The role of AI-powered annotation** ==
30	30
31		-The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
32		-
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).
36		-
37		-This methodology enables:
38		-
39		-* Greater precision in diagnosis.
40		-* Integration of incomplete datasets using AI-driven probabilistic modeling.
41		-* Stratification of patients for personalized treatment.
42		-
43		-== **The Role of AI-Powered Annotation** ==
44		-
45	45	To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
46	46
47	47	* Assigns structured metadata tags to diagnostic features.
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50	50	* Improves AI model transparency through interpretability tools (e.g., SHAP analysis).
51	51	* Facilitates decision-making for clinicians by linking annotations to standardized biomedical ontologies (SNOMED, HPO).
52	52
	43	+Neurodiagnoses provides **two complementary AI-driven diagnostic approaches**:
	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.
	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.
	55	+
	56	+Both systems will be offered for every patient case, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
	57	+
	58	+== **Disease Prediction and Biomarker Estimation** ==
	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	+
53	53	== **The case of neurodegenerative diseases** ==
54	54
55	55	There have been described these 3 diagnostic axes:
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58	58
59	59	* (((
60	60	**Axis 1: Etiology**
61		-
62	62	* //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
63	63	* //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
64	64	* //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
65		-
66		-
67	67	)))
68	68	* (((
69	69	**Axis 2: Molecular Markers**
70		-
71	71	* //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
72	72	* //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
73	73	* //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
74		-
75		-
76	76	)))
77	77	* (((
78	78	**Axis 3: Neuroanatomoclinical**
79		-
80	80	* //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments.
81	81	* //Examples//: Hippocampal atrophy correlating with memory deficits.
82	82	* //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations.
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86	86
87	87	This system enhances:
88	88
89		-* **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials.
	100	+* **Research**: By stratifying patients, reducing cohort heterogeneity in clinical trials.
90	90	* **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
91	91
92	92	== How to Contribute ==
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94	94	* Access the `/docs` folder for guidelines.
95	95	* Use `/code` for the latest AI pipelines.
96	96	* 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]]
97	97
98	98	== Key Objectives ==
99	99
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100	100	* Develop interpretable AI models for diagnosis and progression tracking.
101	101	* Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
102	102	* 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.
103	103
104	104	== Who has access? ==
105	105
106		-We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
	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!
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116	116	{{box title="**Contents**"}}
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129	129	* [[to-do-list>>to-do-list]]
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