Changes for page Neurodiagnoses
Last modified by manuelmenendez on 2025/03/03 22:46
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edited by manuelmenendez
on 2025/02/02 15:11
on 2025/02/02 15:11
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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 ))) ... ... @@ -17,31 +17,21 @@ 17 17 18 18 = **Overview** = 19 19 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. 20 20 21 - Theclassification anddiagnosisof central nervoussystem (CNS) diseaseshavelong 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. 22 22 23 - This projectproposesanewdiagnostic framework—onethat shifts fromsymptom-basedclassificationstoanetiology-driven,tridimensionalsystem. Byintegratinggenetics, proteomics, neuroimaging,computationalmodeling,andAI-poweredannotations,this approachaimsto providemore 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. 24 24 25 - The AI-powered annotationsystemplays a critical role by structuring, interpreting,and tracking multi-modal data, ensuringreal-time disease progression analysis, cliniciandecisionsupport, andpersonalized treatment pathways.26 +On this page, you will find: 26 26 27 -=== **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. 28 28 29 - Neurodiagnosesis an open-source AI-powered diagnostic system designed for complex central nervous system (CNS) disorders, including neurodegenerative diseases, autoimmune encephalopathies, prion disorders, and genetic syndromes.Theproject aims to develop a tridimensionaldiagnosticframeworkwith anAI-powered annotationsystem, integrating etiology, molecular biomarkers, and neuroanatomoclinical correlations for precise, standardized, and scalable CNS disease diagnostics.33 +== **The role of AI-powered annotation** == 30 30 31 -The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on: 32 - 33 -* **Axis 1**: Etiology (genetic or other causes of diseases). 34 -* **Axis 2**: Molecular Markers (biomarkers). 35 -* **Axis 3**: Neuroanatomoclinical correlations (linking clinical symptoms to structural changes in the nervous system). 36 - 37 -This methodology enables: 38 - 39 -* Greater precision in diagnosis. 40 -* Integration of incomplete datasets using AI-driven probabilistic modeling. 41 -* Stratification of patients for personalized treatment. 42 - 43 -== **The Role of AI-Powered Annotation** == 44 - 45 45 To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which: 46 46 47 47 * Assigns structured metadata tags to diagnostic features. ... ... @@ -50,28 +50,34 @@ 50 50 * Improves AI model transparency through interpretability tools (e.g., SHAP analysis). 51 51 * Facilitates decision-making for clinicians by linking annotations to standardized biomedical ontologies (SNOMED, HPO). 52 52 53 -Neurodiagnoses provides two complementary AI-driven diagnostic approaches: 43 +Neurodiagnoses provides **two complementary AI-driven diagnostic approaches**: 54 54 55 -1. Traditional Probabilistic Diagnosis 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. 56 56 57 - *AI provides multiplepossiblediagnoses, each assigned a probability percentage basedonbiomarker, imaging, and clinical data.58 -* ExampleOutput:59 -** 75% Alzheimer'sDisease60 -** 0%LewyBody Dementia61 -** 5% VascularDementia62 -* Usefulfordifferential diagnosis andtreatmentdecision-making.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. 63 63 64 - 2. TridimensionalDiagnosis56 +Both systems will be offered for every patient case, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification. 65 65 66 -* Diagnoses are structured based on: 67 -(1) Etiology (genetic, autoimmune, metabolic, infectious) 68 -(2) Molecular Biomarkers (amyloid-beta, tau, inflammatory markers, EEG patterns) 69 -(3) Neuroanatomoclinical Correlations (brain atrophy, connectivity alterations) 70 -* This approach enables precise disease subtyping and biologically meaningful classification, particularly useful to track progression over time. 58 +== **Disease Prediction and Biomarker Estimation** == 71 71 72 - Forevery patient case, bothsystemswillbe offered,allowingclinicianstocompareAI-generatedprobabilisticdiagnosiswithastructuredtridimensionalclassification.60 +Neurodiagnoses is also implementing **biomarker prediction and disease progression modeling**, using advanced machine learning techniques: 73 73 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. 74 74 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 + 75 75 == **The case of neurodegenerative diseases** == 76 76 77 77 There have been described these 3 diagnostic axes: ... ... @@ -80,25 +80,18 @@ 80 80 81 81 * ((( 82 82 **Axis 1: Etiology** 83 - 84 84 * //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers. 85 85 * //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression. 86 86 * //Tests//: Genetic testing, lifestyle, and cardiovascular screening. 87 - 88 - 89 89 ))) 90 90 * ((( 91 91 **Axis 2: Molecular Markers** 92 - 93 93 * //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression. 94 94 * //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology. 95 95 * //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET). 96 - 97 - 98 98 ))) 99 99 * ((( 100 100 **Axis 3: Neuroanatomoclinical** 101 - 102 102 * //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments. 103 103 * //Examples//: Hippocampal atrophy correlating with memory deficits. 104 104 * //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations. ... ... @@ -108,7 +108,7 @@ 108 108 109 109 This system enhances: 110 110 111 -* **Research**: By stratifying patients, reduc escohort heterogeneity in clinical trials.100 +* **Research**: By stratifying patients, reducing cohort heterogeneity in clinical trials. 112 112 * **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking. 113 113 114 114 == How to Contribute == ... ... @@ -116,6 +116,8 @@ 116 116 * Access the `/docs` folder for guidelines. 117 117 * Use `/code` for the latest AI pipelines. 118 118 * 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]] 119 119 120 120 == Key Objectives == 121 121 ... ... @@ -122,17 +122,17 @@ 122 122 * Develop interpretable AI models for diagnosis and progression tracking. 123 123 * Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources. 124 124 * 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. 125 125 126 126 == Who has access? == 127 127 128 -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! 129 129 ))) 130 130 131 - 132 - 133 - 134 - 135 - 136 136 (% class="col-xs-12 col-sm-4" %) 137 137 ((( 138 138 {{box title="**Contents**"}} ... ... @@ -151,3 +151,4 @@ 151 151 * [[to-do-list>>to-do-list]] 152 152 ))) 153 153 ))) 145 +