Changes for page Neurodiagnoses

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edited by manuelmenendez
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5 -= //A new tridimensional diagnostic framework for complex CNS diseases// =
5 += //A new tridimensional diagnostic framework for CNS diseases// =
6 6  
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.
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.
8 8  We aim to create a structured, interpretable, and scalable diagnostic tool.
9 9  )))
10 10  )))
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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.
21 21  
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.
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.
23 23  
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.
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**.
25 25  
26 -On this page, you will find:
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**.
27 27  
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.
27 +=== **Project Aim** ===
32 32  
33 -== **The role of AI-powered annotation** ==
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**.
34 34  
35 -To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
31 +The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
36 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).
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).
42 42  
43 -Neurodiagnoses provides **two complementary AI-driven diagnostic approaches**:
37 +This methodology enables:
44 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.
39 +* Greater precision in diagnosis.
40 +* Integration of incomplete datasets using AI-driven probabilistic modeling.
41 +* Stratification of patients for personalized treatment.
48 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.
43 +== **The Role of AI-Powered Annotation** ==
55 55  
56 -Both systems will be offered for every patient case, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
45 +To enhance **standardization, interpretability, and clinical application**, the framework integrates **an AI-powered annotation system**, which:
57 57  
58 -== **Disease Prediction and Biomarker Estimation** ==
47 +* **Assigns structured metadata tags** to diagnostic features.
48 +* **Provides real-time contextual explanations** for AI-based classifications.
49 +* **Tracks longitudinal disease progression** using timestamped AI annotations.
50 +* **Improves AI model transparency** through interpretability tools (e.g., SHAP analysis).
51 +* **Facilitates decision-making for clinicians** by linking annotations to standardized biomedical ontologies (SNOMED, HPO).
59 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 71  == **The case of neurodegenerative diseases** ==
72 72  
73 73  There have been described these 3 diagnostic axes:
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76 76  
77 77  * (((
78 78  **Axis 1: Etiology**
61 +
79 79  * //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
80 80  * //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
81 81  * //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
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82 82  )))
83 83  * (((
84 84  **Axis 2: Molecular Markers**
68 +
85 85  * //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
86 86  * //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
87 87  * //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
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88 88  )))
89 89  * (((
90 90  **Axis 3: Neuroanatomoclinical**
75 +
91 91  * //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments.
92 92  * //Examples//: Hippocampal atrophy correlating with memory deficits.
93 93  * //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations.
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97 97  
98 98  This system enhances:
99 99  
100 -* **Research**: By stratifying patients, reducing cohort heterogeneity in clinical trials.
85 +* **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials.
101 101  * **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
102 102  
103 103  == How to Contribute ==
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105 105  * Access the `/docs` folder for guidelines.
106 106  * Use `/code` for the latest AI pipelines.
107 107  * 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]]
110 110  
111 111  == Key Objectives ==
112 112  
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113 113  * Develop interpretable AI models for diagnosis and progression tracking.
114 114  * Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
115 115  * 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.
121 121  
122 122  == Who has access? ==
123 123  
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!
102 +We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
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129 129  {{box title="**Contents**"}}
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142 142  * [[to-do-list>>to-do-list]]
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144 144  )))
145 -