Changes for page Neurodiagnoses
Last modified by manuelmenendez on 2025/03/03 22:46
From version 27.1
edited by manuelmenendez
on 2025/01/29 18:48
on 2025/01/29 18:48
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To version 47.1
edited by manuelmenendez
on 2025/03/03 22:46
on 2025/03/03 22:46
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... ... @@ -2,9 +2,9 @@ 2 2 ((( 3 3 (% class="container" %) 4 4 ((( 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. 9 9 ))) 10 10 ))) ... ... @@ -17,18 +17,57 @@ 17 17 18 18 = **Overview** = 19 19 20 -The //TridimensionalDiagnosticFramework//redefines CNS diseasescan be classified anddiagnosedbyfocusing on: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 -* **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). 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. 25 25 26 - Thismethodologyenables: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. 27 27 28 -* Greater precision in diagnosis. 29 -* Integration of incomplete datasets using AI-driven probabilistic modeling. 30 -* Stratification of patients for personalized treatment. 26 +On this page, you will find: 31 31 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 + 32 32 == **The case of neurodegenerative diseases** == 33 33 34 34 There have been described these 3 diagnostic axes: ... ... @@ -37,7 +37,6 @@ 37 37 38 38 * ((( 39 39 **Axis 1: Etiology** 40 - 41 41 * //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers. 42 42 * //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression. 43 43 * //Tests//: Genetic testing, lifestyle, and cardiovascular screening. ... ... @@ -44,7 +44,6 @@ 44 44 ))) 45 45 * ((( 46 46 **Axis 2: Molecular Markers** 47 - 48 48 * //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression. 49 49 * //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology. 50 50 * //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET). ... ... @@ -51,7 +51,6 @@ 51 51 ))) 52 52 * ((( 53 53 **Axis 3: Neuroanatomoclinical** 54 - 55 55 * //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments. 56 56 * //Examples//: Hippocampal atrophy correlating with memory deficits. 57 57 * //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations. ... ... @@ -61,18 +61,16 @@ 61 61 62 62 This system enhances: 63 63 64 -* **Research**: By stratifying patients, reduc escohort heterogeneity in clinical trials.100 +* **Research**: By stratifying patients, reducing cohort heterogeneity in clinical trials. 65 65 * **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking. 66 66 67 -== Who has access? == 68 - 69 -We welcome contributions from the global community. Let’s build the future of neurological diagnostics together! 70 - 71 71 == How to Contribute == 72 72 73 73 * Access the `/docs` folder for guidelines. 74 74 * Use `/code` for the latest AI pipelines. 75 75 * 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]] 76 76 77 77 == Key Objectives == 78 78 ... ... @@ -79,12 +79,17 @@ 79 79 * Develop interpretable AI models for diagnosis and progression tracking. 80 80 * Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources. 81 81 * Foster collaboration among neuroscientists, AI researchers, and clinicians. 82 -))) 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. 83 83 122 +== Who has access? == 84 84 124 +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 +))) 85 85 86 - 87 - 88 88 (% class="col-xs-12 col-sm-4" %) 89 89 ((( 90 90 {{box title="**Contents**"}} ... ... @@ -98,7 +98,9 @@ 98 98 * `/data`: Sample datasets for testing. 99 99 * `/outputs`: Generated models, visualizations, and reports. 100 100 * [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]] 140 +* [[Notebooks>>Notebooks]] 101 101 * [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]] 102 102 * [[to-do-list>>to-do-list]] 103 103 ))) 104 104 ))) 145 +