Changes for page Neurodiagnoses

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edited by manuelmenendez
on 2025/03/03 22:46
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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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10 10  )))
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17 17  
18 18  = **Overview** =
19 19  
20 -T
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 -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.
23 23  
24 -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.
25 25  
26 -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:
27 27  
28 -=== **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.
29 29  
30 -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** ==
31 31  
32 -The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
35 +To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
33 33  
34 -* **Axis 1**: Etiology (genetic or other causes of diseases).
35 -* **Axis 2**: Molecular Markers (biomarkers).
36 -* **Axis 3**: Neuroanatomoclinical correlations (linking clinical symptoms to structural changes in the nervous system).
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).
37 37  
38 -This methodology enables:
43 +Neurodiagnoses provides **two complementary AI-driven diagnostic approaches**:
39 39  
40 -* Greater precision in diagnosis.
41 -* Integration of incomplete datasets using AI-driven probabilistic modeling.
42 -* Stratification of patients for personalized treatment.
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.
43 43  
44 -== **The Role of AI-Powered Annotation** ==
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.
45 45  
46 -To enhance **standardization, interpretability, and clinical application**, the framework integrates **an AI-powered annotation system**, which:
56 +Both systems will be offered for every patient case, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
47 47  
48 -* **Assigns structured metadata tags** to diagnostic features.
49 -* **Provides real-time contextual explanations** for AI-based classifications.
50 -* **Tracks longitudinal disease progression** using timestamped AI annotations.
51 -* **Improves AI model transparency** through interpretability tools (e.g., SHAP analysis).
52 -* **Facilitates decision-making for clinicians** by linking annotations to standardized biomedical ontologies (SNOMED, HPO).
58 +== **Disease Prediction and Biomarker Estimation** ==
53 53  
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 +
54 54  == **The case of neurodegenerative diseases** ==
55 55  
56 56  There have been described these 3 diagnostic axes:
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59 59  
60 60  * (((
61 61  **Axis 1: Etiology**
62 -
63 63  * //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
64 64  * //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
65 65  * //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
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66 66  )))
67 67  * (((
68 68  **Axis 2: Molecular Markers**
69 -
70 70  * //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
71 71  * //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
72 72  * //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
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73 73  )))
74 74  * (((
75 75  **Axis 3: Neuroanatomoclinical**
76 -
77 77  * //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments.
78 78  * //Examples//: Hippocampal atrophy correlating with memory deficits.
79 79  * //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations.
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83 83  
84 84  This system enhances:
85 85  
86 -* **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials.
100 +* **Research**: By stratifying patients, reducing cohort heterogeneity in clinical trials.
87 87  * **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
88 88  
89 89  == How to Contribute ==
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91 91  * Access the `/docs` folder for guidelines.
92 92  * Use `/code` for the latest AI pipelines.
93 93  * 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]]
94 94  
95 95  == Key Objectives ==
96 96  
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97 97  * Develop interpretable AI models for diagnosis and progression tracking.
98 98  * Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
99 99  * 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.
100 100  
101 101  == Who has access? ==
102 102  
103 -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!
104 104  )))
105 105  
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113 113  {{box title="**Contents**"}}
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121 121  * `/data`: Sample datasets for testing.
122 122  * `/outputs`: Generated models, visualizations, and reports.
123 123  * [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]]
140 +* [[Notebooks>>Notebooks]]
124 124  * [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]]
125 125  * [[to-do-list>>to-do-list]]
126 126  )))
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