Changes for page Neurodiagnoses
Last modified by manuelmenendez on 2025/03/03 22:46
From version 47.1
edited by manuelmenendez
on 2025/03/03 22:46
on 2025/03/03 22:46
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To version 40.1
edited by manuelmenendez
on 2025/02/02 15:12
on 2025/02/02 15:12
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... ... @@ -17,21 +17,25 @@ 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. 21 21 22 - **Neurodiagnoses**redefines thislandscape byintegratingadvancedAIwithmulti-modaldata—includinggenetics,neuroimaging,biomarkers, anddigital healthrecords—to createamore precise, scalable,anddata-drivendiagnosticsystem.21 +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. 23 23 24 - Additionally, **Neurodiagnoses is now expandinginto diseaseprediction andbiomarkerestimation**,integratingstate-of-the-artmachinelearning modelsto enhance precisiondiagnosticsand diseaseprogressionforecasting.23 +Neurodiagnoses is an open-source AI-powered diagnostic system designed for complex CNS disorders, including neurodegenerative diseases, autoimmune encephalopathies, prion disorders, and genetic syndromes. 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. 25 25 26 - On thispage,youwillfind:25 +The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on: 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. 27 +* **Axis 1**: Etiology (genetic or other causes of diseases). 28 +* **Axis 2**: Molecular Markers (biomarkers). 29 +* **Axis 3**: Neuroanatomoclinical correlations (linking clinical symptoms to structural changes in the nervous system). 32 32 33 - == **TheroleofAI-powered annotation** ==31 +This methodology enables: 34 34 33 +* Greater precision in diagnosis. 34 +* Integration of incomplete datasets using AI-driven probabilistic modeling. 35 +* Stratification of patients for personalized treatment. 36 + 37 +== **The Role of AI-Powered Annotation** == 38 + 35 35 To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which: 36 36 37 37 * Assigns structured metadata tags to diagnostic features. ... ... @@ -40,34 +40,28 @@ 40 40 * Improves AI model transparency through interpretability tools (e.g., SHAP analysis). 41 41 * Facilitates decision-making for clinicians by linking annotations to standardized biomedical ontologies (SNOMED, HPO). 42 42 43 -Neurodiagnoses provides **two complementary AI-driven diagnostic approaches**:47 +Neurodiagnoses provides two complementary AI-driven diagnostic approaches: 44 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. 49 +1. Traditional Probabilistic Diagnosis 48 48 49 - 2.**TridimensionalDiagnosis**50 - Diagnosesare structured based on:51 - -**(1)Etiology**(genetic, autoimmune,metabolic, infectious).52 - -**(2)MolecularBiomarkers** (amyloid-beta, tau, inflammatory markers, EEG patterns).53 - -**(3)Neuroanatomoclinical Correlations**(brain atrophy, connectivity alterations).54 - Thisapproachenablesprecisediseasesubtypingandbiologically meaningfulclassification,particularly useful for trackingprogressionover time.51 +* AI provides multiple possible diagnoses, each assigned a probability percentage based on biomarker, imaging, and clinical data. 52 +* Example Output: 53 +** 75% Alzheimer's Disease 54 +** 20% Lewy Body Dementia 55 +** 5% Vascular Dementia 56 +* Useful for differential diagnosis and treatment decision-making. 55 55 56 - Both systems will be offeredfor every patient case, allowing clinicians tocompare AI-generated probabilisticdiagnosiswith a structured tridimensional classification.58 +2. Tridimensional Diagnosis 57 57 58 -== **Disease Prediction and Biomarker Estimation** == 60 +* Diagnoses are structured based on: 61 +(1) Etiology (genetic, autoimmune, metabolic, infectious) 62 +(2) Molecular Biomarkers (amyloid-beta, tau, inflammatory markers, EEG patterns) 63 +(3) Neuroanatomoclinical Correlations (brain atrophy, connectivity alterations) 64 +* This approach enables precise disease subtyping and biologically meaningful classification, particularly useful to track progression over time. 59 59 60 - Neurodiagnoses isalsomplementing**biomarkerpredictionand disease progressionmodeling**, usingadvanced machinelearning techniques:66 +For every patient case, both systems will be offered, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification. 61 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 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: ... ... @@ -76,18 +76,25 @@ 76 76 77 77 * ((( 78 78 **Axis 1: Etiology** 77 + 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. 81 + 82 + 82 82 ))) 83 83 * ((( 84 84 **Axis 2: Molecular Markers** 86 + 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). 90 + 91 + 88 88 ))) 89 89 * ((( 90 90 **Axis 3: Neuroanatomoclinical** 95 + 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.105 +* **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!122 +We welcome contributions from the global community. Let’s build the future of neurological diagnostics together! 125 125 ))) 126 126 125 + 126 + 127 + 128 + 129 + 127 127 (% class="col-xs-12 col-sm-4" %) 128 128 ((( 129 129 {{box title="**Contents**"}} ... ... @@ -142,4 +142,3 @@ 142 142 * [[to-do-list>>to-do-list]] 143 143 ))) 144 144 ))) 145 -