Changes for page Neurodiagnoses
Last modified by manuelmenendez on 2025/03/03 22:46
From version 47.1
edited by manuelmenendez
on 2025/03/03 22:46
on 2025/03/03 22:46
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To version 27.1
edited by manuelmenendez
on 2025/01/29 18:48
on 2025/01/29 18:48
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... ... @@ -2,9 +2,9 @@ 2 2 ((( 3 3 (% class="container" %) 4 4 ((( 5 -= //A new tridimensional diagnostic framework for complexCNS 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 complexCNS 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 ))) ... ... @@ -17,57 +17,18 @@ 17 17 18 18 = **Overview** = 19 19 20 -The classificationnddiagnosisofcentral nervoussystem (CNS)diseaseshave longbeenconstrainedby traditional, phenotype-basedapproaches that often fail to capture the complex interplayofpathophysiological mechanisms, molecular biomarkers, and neuroanatomical changes.20 +The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on: 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. 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). 23 23 24 - Additionally, **Neurodiagnosesis nowexpanding intoisease prediction and biomarker estimation**, integratingstate-of-the-art machine learning models toenhance precision diagnostics and disease progression forecasting.26 +This methodology enables: 25 25 26 -On this page, you will find: 28 +* Greater precision in diagnosis. 29 +* Integration of incomplete datasets using AI-driven probabilistic modeling. 30 +* Stratification of patients for personalized treatment. 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. 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 - 71 71 == **The case of neurodegenerative diseases** == 72 72 73 73 There have been described these 3 diagnostic axes: ... ... @@ -76,6 +76,7 @@ 76 76 77 77 * ((( 78 78 **Axis 1: Etiology** 40 + 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. ... ... @@ -82,6 +82,7 @@ 82 82 ))) 83 83 * ((( 84 84 **Axis 2: Molecular Markers** 47 + 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). ... ... @@ -88,6 +88,7 @@ 88 88 ))) 89 89 * ((( 90 90 **Axis 3: Neuroanatomoclinical** 54 + 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. ... ... @@ -97,16 +97,18 @@ 97 97 98 98 This system enhances: 99 99 100 -* **Research**: By stratifying patients, reduc ingcohort heterogeneity in clinical trials.64 +* **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 67 +== Who has access? == 68 + 69 +We welcome contributions from the global community. Let’s build the future of neurological diagnostics together! 70 + 103 103 == How to Contribute == 104 104 105 105 * 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 ... ... @@ -113,17 +113,12 @@ 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. 82 +))) 121 121 122 -== 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! 125 -))) 126 126 86 + 87 + 127 127 (% class="col-xs-12 col-sm-4" %) 128 128 ((( 129 129 {{box title="**Contents**"}} ... ... @@ -137,9 +137,7 @@ 137 137 * `/data`: Sample datasets for testing. 138 138 * `/outputs`: Generated models, visualizations, and reports. 139 139 * [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]] 140 -* [[Notebooks>>Notebooks]] 141 141 * [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]] 142 142 * [[to-do-list>>to-do-list]] 143 143 ))) 144 144 ))) 145 -