Changes for page Neurodiagnoses
Last modified by manuelmenendez on 2025/03/03 22:46
From version 24.1
edited by manuelmenendez
on 2025/01/29 18:46
on 2025/01/29 18:46
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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 ))) ... ... @@ -15,31 +15,67 @@ 15 15 ((( 16 16 = What is this about and what can I find here? = 17 17 18 -= =**Overview** ==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 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. 26 26 27 - [[For instance,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="tridimensionalview ofeurodegenerative diseases"]]26 +On this page, you will find: 28 28 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. 29 29 30 -Th is methodologyenables:33 +== **The role of AI-powered annotation** == 31 31 32 -* Greater precision in diagnosis. 33 -* Integration of incomplete datasets using AI-driven probabilistic modeling. 34 -* Stratification of patients for personalized treatment. 35 +To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which: 35 35 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 + 36 36 == **The case of neurodegenerative diseases** == 37 37 38 38 There have been described these 3 diagnostic axes: 39 39 75 +[[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"]] 76 + 40 40 * ((( 41 41 **Axis 1: Etiology** 42 - 43 43 * //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers. 44 44 * //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression. 45 45 * //Tests//: Genetic testing, lifestyle, and cardiovascular screening. ... ... @@ -46,7 +46,6 @@ 46 46 ))) 47 47 * ((( 48 48 **Axis 2: Molecular Markers** 49 - 50 50 * //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression. 51 51 * //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology. 52 52 * //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET). ... ... @@ -53,7 +53,6 @@ 53 53 ))) 54 54 * ((( 55 55 **Axis 3: Neuroanatomoclinical** 56 - 57 57 * //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments. 58 58 * //Examples//: Hippocampal atrophy correlating with memory deficits. 59 59 * //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations. ... ... @@ -63,18 +63,16 @@ 63 63 64 64 This system enhances: 65 65 66 -* **Research**: By stratifying patients, reduc escohort heterogeneity in clinical trials.100 +* **Research**: By stratifying patients, reducing cohort heterogeneity in clinical trials. 67 67 * **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking. 68 68 69 -== Who has access? == 70 - 71 -We welcome contributions from the global community. Let’s build the future of neurological diagnostics together! 72 - 73 73 == How to Contribute == 74 74 75 75 * Access the `/docs` folder for guidelines. 76 76 * Use `/code` for the latest AI pipelines. 77 77 * 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]] 78 78 79 79 == Key Objectives == 80 80 ... ... @@ -81,9 +81,17 @@ 81 81 * Develop interpretable AI models for diagnosis and progression tracking. 82 82 * Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources. 83 83 * Foster collaboration among neuroscientists, AI researchers, and clinicians. 84 -))) 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. 85 85 122 +== Who has access? == 86 86 124 +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 + 87 87 (% class="col-xs-12 col-sm-4" %) 88 88 ((( 89 89 {{box title="**Contents**"}} ... ... @@ -97,7 +97,9 @@ 97 97 * `/data`: Sample datasets for testing. 98 98 * `/outputs`: Generated models, visualizations, and reports. 99 99 * [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]] 140 +* [[Notebooks>>Notebooks]] 100 100 * [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]] 101 101 * [[to-do-list>>to-do-list]] 102 102 ))) 103 103 ))) 145 +