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5		-= //A new tridimensional diagnostic framework for CNS conditions// =
	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 neurological 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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16	16	= What is this about and what can I find here? =
17	17
18		-== **Overview** ==
	18	+= **Overview** =
19	19
20		-The //Tridimensional Diagnostic Framework// redefines how neurodegenerative diseases (NDDs) are classified by focusing on:
	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		-* **Axis 1**: Etiology (genetic/sporadic and environmental factors).
23		-* **Axis 2**: Molecular Markers (biomarkers and proteinopathies).
24		-* **Axis 3**: Neuroanatomoclinical correlations (linking clinical symptoms to structural changes in the nervous system).
	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.
25	25
26		-This methodology enables:
	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.
27	27
28		-* Greater precision in diagnosis.
29		-* Integration of incomplete datasets using AI-driven probabilistic modeling.
30		-* Stratification of patients for personalized treatment.
	26	+On this page, you will find:
31	31
32		-== **Diagnostic Axes** ==
	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.
33	33
	33	+== **The role of AI-powered annotation** ==
	34	+
	35	+To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
	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).
	42	+
	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	+
	71	+== **The case of neurodegenerative diseases** ==
	72	+
	73	+There have been described these 3 diagnostic axes:
	74	+
	75	+[[Neurodegenerative diseases can be studied and classified in a tridimensional scheme with three axes: anatomic–clinical, molecular, and etiologic. CSF, cerebrospinal fluid; FDG, fluorodeoxyglucose; MRI, magnetic resonance imaging; PET, positron emission tomography.>>image:tridimensional.png||alt="tridimensional view of neurodegenerative diseases"]]
	76	+
34	34	* (((
35	35	**Axis 1: Etiology**
36		-
37	37	* //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
38	38	* //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
39		-* //Tests//: Genetic testing, lifestyle and cardiovascular screening.
	81	+* //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
40	40	)))
41	41	* (((
42	42	**Axis 2: Molecular Markers**
43		-
44	44	* //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
45	45	* //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
46	46	* //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
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47	47	)))
48	48	* (((
49	49	**Axis 3: Neuroanatomoclinical**
50		-
51	51	* //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments.
52	52	* //Examples//: Hippocampal atrophy correlating with memory deficits.
53	53	* //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations.
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57	57
58	58	This system enhances:
59	59
60		-* **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials.
	100	+* **Research**: By stratifying patients, reducing cohort heterogeneity in clinical trials.
61	61	* **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
62	62
63		-== Who has access? ==
64		-
65		-We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
66		-
67	67	== How to Contribute ==
68	68
69	69	* Access the `/docs` folder for guidelines.
70	70	* Use `/code` for the latest AI pipelines.
71	71	* 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]]
72	72
73	73	== Key Objectives ==
74	74
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75	75	* Develop interpretable AI models for diagnosis and progression tracking.
76	76	* Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
77	77	* Foster collaboration among neuroscientists, AI researchers, and clinicians.
78		-)))
	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.
79	79
	122	+== Who has access? ==
80	80
	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!
	125	+)))
	126	+
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83	83	{{box title="**Contents**"}}
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90	90	* `/code`: Machine learning pipelines and scripts.
91	91	* `/data`: Sample datasets for testing.
92	92	* `/outputs`: Generated models, visualizations, and reports.
	139	+* [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]]
	140	+* [[Notebooks>>Notebooks]]
	141	+* [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]]
	142	+* [[to-do-list>>to-do-list]]
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tridimensional.png

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	1	+XWiki.manuelmenendez

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