Changes for page Neurodiagnoses
Last modified by manuelmenendez on 2025/03/03 22:46
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edited by manuelmenendez
on 2025/03/03 22:46
on 2025/03/03 22:46
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To version 33.1
edited by manuelmenendez
on 2025/02/01 13:54
on 2025/02/01 13:54
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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 +17,40 @@ 17 17 18 18 = **Overview** = 19 19 20 -T he 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.20 +T 21 21 22 - **Neurodiagnoses** redefinesthislandscape byintegratingadvancedAIwithmulti-modaldata—includinggenetics,neuroimaging,biomarkers, anddigital health records—tocreate amoreprecise, scalable, anddata-driven diagnostic system.22 +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. **Neurodegenerative and psychiatric disorders**, for example, exhibit significant **clinical overlap, co-pathology, and heterogeneity**, making current diagnostic models insufficient. 23 23 24 - Additionally,**Neurodiagnosesisnowxpandingintodiseaseprediction andbiomarkerestimation**,integrating state-of-the-artmachinelearningmodels to enhanceprecisiondiagnostics anddisease progression forecasting.24 +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**. 25 25 26 - Onthis page,youwillfind:26 +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**. 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. 28 +=== **Project Aim** === 32 32 33 - == **The role of AI-powered annotation**==30 +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**. 34 34 35 -T o enhancestandardization, interpretability, and clinicalapplication,the framework integrates anAI-powered annotationystem,which:32 +The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on: 36 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). 34 +* **Axis 1**: Etiology (genetic or other causes of diseases). 35 +* **Axis 2**: Molecular Markers (biomarkers). 36 +* **Axis 3**: Neuroanatomoclinical correlations (linking clinical symptoms to structural changes in the nervous system). 42 42 43 - Neurodiagnosesprovides **twocomplementaryAI-drivendiagnostic approaches**:38 +This methodology enables: 44 44 45 - 1.**ProbabilisticDiagnosis**46 - AIassigns probability scorestomultiplepossible diagnosesbased onbiomarker,imaging,and clinicaldata.47 - * Usefulfordifferentialdiagnosis and treatmentdecision-making.40 +* Greater precision in diagnosis. 41 +* Integration of incomplete datasets using AI-driven probabilistic modeling. 42 +* Stratification of patients for personalized treatment. 48 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. 44 +== **The Role of AI-Powered Annotation** == 55 55 56 - Bothsystems will beofferedforeverypatientcase, allowingclinicianstocompareAI-generated probabilisticdiagnosiswith a structured tridimensionalclassification.46 +To enhance **standardization, interpretability, and clinical application**, the framework integrates **an AI-powered annotation system**, which: 57 57 58 -== **Disease Prediction and Biomarker Estimation** == 48 +* **Assigns structured metadata tags** to diagnostic features. 49 +* **Provides real-time contextual explanations** for AI-based classifications. 50 +* **Tracks longitudinal disease progression** using timestamped AI annotations. 51 +* **Improves AI model transparency** through interpretability tools (e.g., SHAP analysis). 52 +* **Facilitates decision-making for clinicians** by linking annotations to standardized biomedical ontologies (SNOMED, HPO). 59 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** 62 + 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** 69 + 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** 76 + 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. ... ... @@ -97,7 +97,7 @@ 97 97 98 98 This system enhances: 99 99 100 -* **Research**: By stratifying patients, reduc ingcohort heterogeneity in clinical trials.86 +* **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials. 101 101 * **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking. 102 102 103 103 == How to Contribute == ... ... @@ -105,8 +105,6 @@ 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 +113,17 @@ 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. 121 121 122 122 == 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!103 +We welcome contributions from the global community. Let’s build the future of neurological diagnostics together! 125 125 ))) 126 126 106 + 107 + 108 + 109 + 110 + 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 -