Changes for page Neurodiagnoses
Last modified by manuelmenendez on 2025/03/03 22:46
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edited by manuelmenendez
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on 2025/02/02 15:09
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To version 5.1
edited by manuelmenendez
on 2025/01/27 23:04
on 2025/01/27 23:04
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... ... @@ -2,10 +2,9 @@ 2 2 ((( 3 3 (% class="container" %) 4 4 ((( 5 -= //A new tridimensionaldiagnostic framework for CNS diseases//=5 += Neurodiagnoses = 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. 8 -We aim to create a structured, interpretable, and scalable diagnostic tool. 7 +This project is focused on developing a novel nosological and diagnostic framework for neurological diseases. Using advanced AI techniques and integrating data from neuroimaging, biomarkers, and biomedical ontologies, we aim to create a structured, interpretable, and scalable diagnostic tool. 9 9 ))) 10 10 ))) 11 11 ... ... @@ -13,131 +13,26 @@ 13 13 ((( 14 14 (% class="col-xs-12 col-sm-8" %) 15 15 ((( 16 -= What is this about and whatcan I find here? =15 += What can I find here? = 17 17 18 -= **Overview**=17 +== Who has access? == 19 19 19 +We welcome contributions from the global community. Let’s build the future of neurological diagnostics together! 20 20 21 - Theclassificationand diagnosis of central nervoussystem (CNS) diseases have long been constrainedby traditional phenotype-based approaches, which often fail to capturethecomplex 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.21 +== How to Contribute: == 22 22 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. 24 - 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. 26 - 27 -=== **Project Aim** === 28 - 29 -Neurodiagnoses is an open-source AI-powered diagnostic system designed for complex central nervous system (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. 30 - 31 -The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on: 32 - 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). 36 - 37 -This methodology enables: 38 - 39 -* Greater precision in diagnosis. 40 -* Integration of incomplete datasets using AI-driven probabilistic modeling. 41 -* Stratification of patients for personalized treatment. 42 - 43 -== **The Role of AI-Powered Annotation** == 44 - 45 -To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which: 46 - 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). 52 - 53 -Neurodiagnoses provides two complementary AI-driven diagnostic approaches: 54 - 55 -1. Traditional Probabilistic Diagnosis 56 - 57 -* AI provides multiple possible diagnoses, each assigned a probability percentage based on biomarker, imaging, and clinical data. 58 -* Example Output: 59 - 60 -> 61 - 62 -{{{75% Alzheimer's Disease 63 -20% Lewy Body Dementia 64 -5% Vascular Dementia 65 -}}} 66 - 67 -* Useful for differential diagnosis and treatment decision-making. 68 - 69 -2. Tridimensional Diagnosis 70 - 71 -* Diagnoses are structured based on: 72 -(1) Etiology (genetic, autoimmune, metabolic, infectious) 73 -(2) Molecular Biomarkers (amyloid-beta, tau, inflammatory markers, EEG patterns) 74 -(3) Neuroanatomoclinical Correlations (brain atrophy, connectivity alterations) 75 -* This approach enables precise disease subtyping and biologically meaningful classification. 76 - 77 -For every patient case, both systems will be offered, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification. 78 - 79 - 80 -== **The case of neurodegenerative diseases** == 81 - 82 -There have been described these 3 diagnostic axes: 83 - 84 -[[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"]] 85 - 86 -* ((( 87 -**Axis 1: Etiology** 88 - 89 -* //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers. 90 -* //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression. 91 -* //Tests//: Genetic testing, lifestyle, and cardiovascular screening. 92 - 93 - 94 -))) 95 -* ((( 96 -**Axis 2: Molecular Markers** 97 - 98 -* //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression. 99 -* //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology. 100 -* //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET). 101 - 102 - 103 -))) 104 -* ((( 105 -**Axis 3: Neuroanatomoclinical** 106 - 107 -* //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments. 108 -* //Examples//: Hippocampal atrophy correlating with memory deficits. 109 -* //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations. 110 -))) 111 - 112 -== **Applications** == 113 - 114 -This system enhances: 115 - 116 -* **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials. 117 -* **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking. 118 - 119 -== How to Contribute == 120 - 121 121 * Access the `/docs` folder for guidelines. 122 122 * Use `/code` for the latest AI pipelines. 123 123 * Share feedback and ideas in the wiki discussion pages. 124 124 125 -== Key Objectives == 27 +== Key Objectives: == 126 126 127 127 * Develop interpretable AI models for diagnosis and progression tracking. 128 128 * Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources. 129 129 * Foster collaboration among neuroscientists, AI researchers, and clinicians. 130 - 131 -== Who has access? == 132 - 133 -We welcome contributions from the global community. Let’s build the future of neurological diagnostics together! 134 134 ))) 135 135 136 136 137 - 138 - 139 - 140 - 141 141 (% class="col-xs-12 col-sm-4" %) 142 142 ((( 143 143 {{box title="**Contents**"}} ... ... @@ -144,15 +144,11 @@ 144 144 {{toc/}} 145 145 {{/box}} 146 146 147 -== Main contents == 41 +== Main contents: == 148 148 149 149 * `/docs`: Documentation and contribution guidelines. 150 150 * `/code`: Machine learning pipelines and scripts. 151 151 * `/data`: Sample datasets for testing. 152 152 * `/outputs`: Generated models, visualizations, and reports. 153 -* [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]] 154 -* [[Notebooks>>Notebooks]] 155 -* [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]] 156 -* [[to-do-list>>to-do-list]] 157 157 ))) 158 158 )))
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