Changes for page Neurodiagnoses

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edited by manuelmenendez
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edited by manuelmenendez
on 2025/03/03 22:46
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2 2  (((
3 3  (% class="container" %)
4 4  (((
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.
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 CNS diseases can be classified and diagnosed 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 or other causes of diseases).
23 -* **Axis 2**: Molecular Markers (biomarkers).
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  
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.
26 26  
27 -This methodology enables:
26 +On this page, you will find:
28 28  
29 -* Greater precision in diagnosis.
30 -* Integration of incomplete datasets using AI-driven probabilistic modeling.
31 -* Stratification of patients for personalized treatment.
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.
32 32  
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 +
33 33  == **The case of neurodegenerative diseases** ==
34 34  
35 35  There have been described these 3 diagnostic axes:
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38 38  
39 39  * (((
40 40  **Axis 1: Etiology**
41 -
42 42  * //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
43 43  * //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
44 44  * //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
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45 45  )))
46 46  * (((
47 47  **Axis 2: Molecular Markers**
48 -
49 49  * //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
50 50  * //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
51 51  * //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
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52 52  )))
53 53  * (((
54 54  **Axis 3: Neuroanatomoclinical**
55 -
56 56  * //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments.
57 57  * //Examples//: Hippocampal atrophy correlating with memory deficits.
58 58  * //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations.
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62 62  
63 63  This system enhances:
64 64  
65 -* **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials.
100 +* **Research**: By stratifying patients, reducing cohort heterogeneity in clinical trials.
66 66  * **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
67 67  
68 -== Who has access? ==
69 -
70 -We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
71 -
72 72  == How to Contribute ==
73 73  
74 74  * Access the `/docs` folder for guidelines.
75 75  * Use `/code` for the latest AI pipelines.
76 76  * 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]]
77 77  
78 78  == Key Objectives ==
79 79  
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80 80  * Develop interpretable AI models for diagnosis and progression tracking.
81 81  * Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
82 82  * Foster collaboration among neuroscientists, AI researchers, and clinicians.
83 -)))
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.
84 84  
122 +== Who has access? ==
85 85  
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 +
86 86  (% class="col-xs-12 col-sm-4" %)
87 87  (((
88 88  {{box title="**Contents**"}}
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96 96  * `/data`: Sample datasets for testing.
97 97  * `/outputs`: Generated models, visualizations, and reports.
98 98  * [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]]
140 +* [[Notebooks>>Notebooks]]
99 99  * [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]]
100 100  * [[to-do-list>>to-do-list]]
101 101  )))
102 102  )))
145 +