Changes for page Neurodiagnoses
Last modified by manuelmenendez on 2025/03/03 22:46
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edited by manuelmenendez
on 2025/02/01 13:54
on 2025/02/01 13:54
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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 ))) ... ... @@ -17,40 +17,57 @@ 17 17 18 18 = **Overview** = 19 19 20 -T 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 - Theclassification anddiagnosisof**centralnervous system (CNS)diseases**havelong been constrainedby**traditionalphenotype-basedpproaches**,whichoften failcapture the **complexpathophysiologicalmechanisms,molecular biomarkers, andneuroanatomicalchanges** thatdrivedisease progression.**Neurodegenerative andpsychiatric disorders**,forxample,exhibitsignificant **clinicaloverlap,co-pathology, andheterogeneity**, making currentdiagnosticmodelsinsufficient.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. 23 23 24 - This projectproposesa **newdiagnosticframework**—onethat**shifts fromsymptom-basedclassifications**toan**etiology-driven,tridimensional system**.Byintegrating**genetics, proteomics, neuroimaging,computationalmodeling,andAI-poweredannotations**,this approachaimsto provide**more precise,scalable, and biologically groundedmethodfordiagnosingand managing CNS diseases**.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. 25 25 26 - The **AI-powered annotationsystem**plays a critical role by **structuring, interpreting,and tracking multi-modal data**, ensuring**real-time disease progression analysis, cliniciandecisionsupport, andpersonalized treatment pathways**.26 +On this page, you will find: 27 27 28 -=== **Project Aim** === 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 -The project aims to develop a **tridimensionaldiagnosticframework**with an **AI-powered annotationsystem**,integrating **etiology, molecular biomarkers, and neuroanatomoclinical correlations** for **precise, standardized, and scalable CNS disease diagnostics**.33 +== **The role of AI-powered annotation** == 31 31 32 -The //TridimensionalDiagnosticFramework//redefines CNS diseasescanbeclassified and diagnosedbyfocusingon:35 +To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which: 33 33 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). 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). 37 37 38 - Thismethodology enables:43 +Neurodiagnoses provides **two complementary AI-driven diagnostic approaches**: 39 39 40 - *Greater precisionin diagnosis.41 -* I ntegration ofincomplete datasetsusingAI-driven probabilisticmodeling.42 - *Stratificationofpatients forpersonalized treatment.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. 43 43 44 -== **The Role of AI-Powered Annotation** == 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. 45 45 46 - Toenhance**standardization,interpretability, andclinical application**,theframeworkintegrates**anAI-poweredannotation system**, which:56 +Both systems will be offered for every patient case, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification. 47 47 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). 58 +== **Disease Prediction and Biomarker Estimation** == 53 53 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 + 54 54 == **The case of neurodegenerative diseases** == 55 55 56 56 There have been described these 3 diagnostic axes: ... ... @@ -59,7 +59,6 @@ 59 59 60 60 * ((( 61 61 **Axis 1: Etiology** 62 - 63 63 * //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers. 64 64 * //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression. 65 65 * //Tests//: Genetic testing, lifestyle, and cardiovascular screening. ... ... @@ -66,7 +66,6 @@ 66 66 ))) 67 67 * ((( 68 68 **Axis 2: Molecular Markers** 69 - 70 70 * //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression. 71 71 * //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology. 72 72 * //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET). ... ... @@ -73,7 +73,6 @@ 73 73 ))) 74 74 * ((( 75 75 **Axis 3: Neuroanatomoclinical** 76 - 77 77 * //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments. 78 78 * //Examples//: Hippocampal atrophy correlating with memory deficits. 79 79 * //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations. ... ... @@ -83,7 +83,7 @@ 83 83 84 84 This system enhances: 85 85 86 -* **Research**: By stratifying patients, reduc escohort heterogeneity in clinical trials.100 +* **Research**: By stratifying patients, reducing cohort heterogeneity in clinical trials. 87 87 * **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking. 88 88 89 89 == How to Contribute == ... ... @@ -91,6 +91,8 @@ 91 91 * Access the `/docs` folder for guidelines. 92 92 * Use `/code` for the latest AI pipelines. 93 93 * 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]] 94 94 95 95 == Key Objectives == 96 96 ... ... @@ -97,17 +97,17 @@ 97 97 * Develop interpretable AI models for diagnosis and progression tracking. 98 98 * Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources. 99 99 * 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. 100 100 101 101 == Who has access? == 102 102 103 -We welcome contributions from the global community. Let’s build the future of neurological diagnostics together! 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! 104 104 ))) 105 105 106 - 107 - 108 - 109 - 110 - 111 111 (% class="col-xs-12 col-sm-4" %) 112 112 ((( 113 113 {{box title="**Contents**"}} ... ... @@ -121,7 +121,9 @@ 121 121 * `/data`: Sample datasets for testing. 122 122 * `/outputs`: Generated models, visualizations, and reports. 123 123 * [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]] 140 +* [[Notebooks>>Notebooks]] 124 124 * [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]] 125 125 * [[to-do-list>>to-do-list]] 126 126 ))) 127 127 ))) 145 +