Wiki source code of Neurodiagnoses
Version 40.1 by manuelmenendez on 2025/02/02 15:12
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5 | = //A new tridimensional diagnostic framework for complex CNS diseases// = | ||
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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 | We aim to create a structured, interpretable, and scalable diagnostic tool. | ||
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16 | = What is this about and what can I find here? = | ||
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18 | = **Overview** = | ||
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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. | ||
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23 | Neurodiagnoses is an open-source AI-powered diagnostic system designed for complex CNS disorders, including neurodegenerative diseases, autoimmune encephalopathies, prion disorders, and genetic syndromes. 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. | ||
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25 | The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on: | ||
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27 | * **Axis 1**: Etiology (genetic or other causes of diseases). | ||
28 | * **Axis 2**: Molecular Markers (biomarkers). | ||
29 | * **Axis 3**: Neuroanatomoclinical correlations (linking clinical symptoms to structural changes in the nervous system). | ||
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31 | This methodology enables: | ||
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33 | * Greater precision in diagnosis. | ||
34 | * Integration of incomplete datasets using AI-driven probabilistic modeling. | ||
35 | * Stratification of patients for personalized treatment. | ||
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37 | == **The Role of AI-Powered Annotation** == | ||
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39 | To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which: | ||
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41 | * Assigns structured metadata tags to diagnostic features. | ||
42 | * Provides real-time contextual explanations for AI-based classifications. | ||
43 | * Tracks longitudinal disease progression using timestamped AI annotations. | ||
44 | * Improves AI model transparency through interpretability tools (e.g., SHAP analysis). | ||
45 | * Facilitates decision-making for clinicians by linking annotations to standardized biomedical ontologies (SNOMED, HPO). | ||
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47 | Neurodiagnoses provides two complementary AI-driven diagnostic approaches: | ||
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49 | 1. Traditional Probabilistic Diagnosis | ||
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51 | * AI provides multiple possible diagnoses, each assigned a probability percentage based on biomarker, imaging, and clinical data. | ||
52 | * Example Output: | ||
53 | ** 75% Alzheimer's Disease | ||
54 | ** 20% Lewy Body Dementia | ||
55 | ** 5% Vascular Dementia | ||
56 | * Useful for differential diagnosis and treatment decision-making. | ||
57 | |||
58 | 2. Tridimensional Diagnosis | ||
59 | |||
60 | * Diagnoses are structured based on: | ||
61 | (1) Etiology (genetic, autoimmune, metabolic, infectious) | ||
62 | (2) Molecular Biomarkers (amyloid-beta, tau, inflammatory markers, EEG patterns) | ||
63 | (3) Neuroanatomoclinical Correlations (brain atrophy, connectivity alterations) | ||
64 | * This approach enables precise disease subtyping and biologically meaningful classification, particularly useful to track progression over time. | ||
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66 | For every patient case, both systems will be offered, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification. | ||
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69 | == **The case of neurodegenerative diseases** == | ||
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71 | There have been described these 3 diagnostic axes: | ||
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73 | [[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"]] | ||
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75 | * ((( | ||
76 | **Axis 1: Etiology** | ||
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78 | * //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers. | ||
79 | * //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression. | ||
80 | * //Tests//: Genetic testing, lifestyle, and cardiovascular screening. | ||
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84 | * ((( | ||
85 | **Axis 2: Molecular Markers** | ||
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87 | * //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression. | ||
88 | * //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology. | ||
89 | * //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET). | ||
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94 | **Axis 3: Neuroanatomoclinical** | ||
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96 | * //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments. | ||
97 | * //Examples//: Hippocampal atrophy correlating with memory deficits. | ||
98 | * //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations. | ||
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101 | == **Applications** == | ||
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103 | This system enhances: | ||
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105 | * **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials. | ||
106 | * **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking. | ||
107 | |||
108 | == How to Contribute == | ||
109 | |||
110 | * Access the `/docs` folder for guidelines. | ||
111 | * Use `/code` for the latest AI pipelines. | ||
112 | * Share feedback and ideas in the wiki discussion pages. | ||
113 | |||
114 | == Key Objectives == | ||
115 | |||
116 | * Develop interpretable AI models for diagnosis and progression tracking. | ||
117 | * Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources. | ||
118 | * Foster collaboration among neuroscientists, AI researchers, and clinicians. | ||
119 | |||
120 | == Who has access? == | ||
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122 | We welcome contributions from the global community. Let’s build the future of neurological diagnostics together! | ||
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132 | {{box title="**Contents**"}} | ||
133 | {{toc/}} | ||
134 | {{/box}} | ||
135 | |||
136 | == Main contents == | ||
137 | |||
138 | * `/docs`: Documentation and contribution guidelines. | ||
139 | * `/code`: Machine learning pipelines and scripts. | ||
140 | * `/data`: Sample datasets for testing. | ||
141 | * `/outputs`: Generated models, visualizations, and reports. | ||
142 | * [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]] | ||
143 | * [[Notebooks>>Notebooks]] | ||
144 | * [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]] | ||
145 | * [[to-do-list>>to-do-list]] | ||
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