Changes for page Neurodiagnoses

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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.
9 9  )))
10 10  )))
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15 15  (((
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.
54 54  )))
55 55  
56 -==== **Case Studies** ====
96 +== **Applications** ==
57 57  
58 -1. (((
59 -**Sporadic Alzheimer’s Disease**:
60 -
61 -* Axis 1: Sporadic (ApoE4, poor sleep habits).
62 -* Axis 2: Amyloid-beta plaques, elevated NFL.
63 -* Axis 3: Right hippocampus atrophy (visual memory loss).
64 -)))
65 -1. (((
66 -**Genetic Parkinson’s Disease**:
67 -
68 -* Axis 1: Genetic (LRRK2 mutation).
69 -* Axis 2: Alpha-synuclein aggregation.
70 -* Axis 3: Substantia nigra degeneration (motor dysfunction).
71 -)))
72 -
73 -==== **Applications** ====
74 -
75 75  This system enhances:
76 76  
77 -* **Research**: By stratifying patients, it reduces cohort heterogeneity in clinical trials.
100 +* **Research**: By stratifying patients, reducing cohort heterogeneity in clinical trials.
78 78  * **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
79 79  
80 -== Who has access? ==
103 +== How to Contribute ==
81 81  
82 -We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
83 -
84 -== How to Contribute: ==
85 -
86 86  * Access the `/docs` folder for guidelines.
87 87  * Use `/code` for the latest AI pipelines.
88 88  * 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]]
89 89  
90 -== Key Objectives: ==
111 +== Key Objectives ==
91 91  
92 92  * Develop interpretable AI models for diagnosis and progression tracking.
93 93  * Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
94 94  * Foster collaboration among neuroscientists, AI researchers, and clinicians.
95 -)))
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.
96 96  
122 +== Who has access? ==
97 97  
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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100 100  {{box title="**Contents**"}}
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101 101  {{toc/}}
102 102  {{/box}}
103 103  
104 -== Main contents: ==
133 +== Main contents ==
105 105  
106 106  * `/docs`: Documentation and contribution guidelines.
107 107  * `/code`: Machine learning pipelines and scripts.
108 108  * `/data`: Sample datasets for testing.
109 109  * `/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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111 111  )))
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