Changes for page Neurodiagnoses
Last modified by manuelmenendez on 2025/03/03 22:46
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edited by manuelmenendez
on 2025/02/08 17:20
on 2025/02/08 17:20
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To version 34.1
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 ))) ... ... @@ -18,45 +18,38 @@ 18 18 = **Overview** = 19 19 20 20 21 -The classification and diagnosis of central nervous system (CNS) diseases have long been constrained by traditional ,phenotype-based approachesthatoften fail to capture the complexinterplay of pathophysiological mechanisms, molecular biomarkers, and neuroanatomical changes.NeurodiagnosesredefinesthislandscapebyintegratingadvancedAI withmulti-modal data—including genetics,neuroimaging, biomarkers,anddigitalhealthrecords—tocreatea moreprecise,scalable, anddata-driven diagnostic system.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. **Neurodegenerative and psychiatric disorders**, for example, exhibit significant **clinical overlap, co-pathology, and heterogeneity**, making current diagnostic models insufficient. 22 22 23 - In additiontotheseclinicaldiagnosticapproaches, Neurodiagnoseshas expanded intoa research-orientedplatform through the integrationof**CNSDigitalTwins**. This cutting-edge conceptinvolves creatinga personalizeddigital replicaofapatient’sCNS by incorporatingmulti-omicsdata (proteomics,genomics,lipidomics,transcriptomics), variousneuroimagingmodalities, and digitalhealth information. Thesedigital twinsenablesimulations ofdiseaseprogression,support thediscoveryof novelbiomarkers, andhelpidentify newtherapeutictargets.23 +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**. 24 24 25 - Onthis page,youwillfind:25 +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**. 26 26 27 -* Detailed descriptions of both the clinical diagnostic tools and the research framework. 28 -* Access to our AI models, data processing pipelines, and digital twin simulations. 29 -* Collaborative resources for researchers, clinicians, and AI developers. 30 -* Guidelines and instructions on how to contribute to and expand the project. 27 +=== **Project Aim** === 31 31 32 - == **The role of AI-powered annotation**==29 +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**. 33 33 34 -T o enhancestandardization, interpretability, and clinicalapplication,the framework integrates anAI-powered annotationystem,which:31 +The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on: 35 35 36 -* Assign structured metadata tags to diagnostic features. 37 -* Provides real-time contextual explanations for AI-based classifications. 38 -* Tracks longitudinal disease progression using timestamped AI annotations. 39 -* Improves AI model transparency through interpretability tools (e.g., SHAP analysis). 40 -* Facilitates decision-making for clinicians by linking annotations to standardized biomedical ontologies (SNOMED, HPO). 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). 41 41 42 - NeurodiagnosesprovidestwocomplementaryAI-drivendiagnostic approaches:37 +This methodology enables: 43 43 44 -1. Traditional Probabilistic Diagnosis 39 +* Greater precision in diagnosis. 40 +* Integration of incomplete datasets using AI-driven probabilistic modeling. 41 +* Stratification of patients for personalized treatment. 45 45 46 -* AI provides multiple possible diagnoses, each assigned a probability percentage based on biomarker, imaging, and clinical data. 47 -* Useful for differential diagnosis and treatment decision-making. 43 +== **The Role of AI-Powered Annotation** == 48 48 49 - 2.TridimensionalDiagnosis45 +To enhance **standardization, interpretability, and clinical application**, the framework integrates **an AI-powered annotation system**, which: 50 50 51 -* Diagnosesarestructuredbased on:52 - (1)Etiology (genetic,autoimmune,metabolic,infectious)53 - (2)MolecularBiomarkers(amyloid-beta, tau,inflammatorymarkers,EEGpatterns)54 - (3)NeuroanatomoclinicalCorrelations(brainatrophy,connectivityalterations)55 -* Thisapproach enablesprecise disease subtypingand biologicallymeaningfulclassification, particularly usefulfortracking progression overtime.47 +* **Assigns structured metadata tags** to diagnostic features. 48 +* **Provides real-time contextual explanations** for AI-based classifications. 49 +* **Tracks longitudinal disease progression** using timestamped AI annotations. 50 +* **Improves AI model transparency** through interpretability tools (e.g., SHAP analysis). 51 +* **Facilitates decision-making for clinicians** by linking annotations to standardized biomedical ontologies (SNOMED, HPO). 56 56 57 -Both systems will be offered for every patient case, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification. 58 - 59 - 60 60 == **The case of neurodegenerative diseases** == 61 61 62 62 There have been described these 3 diagnostic axes: ... ... @@ -69,8 +69,6 @@ 69 69 * //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers. 70 70 * //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression. 71 71 * //Tests//: Genetic testing, lifestyle, and cardiovascular screening. 72 - 73 - 74 74 ))) 75 75 * ((( 76 76 **Axis 2: Molecular Markers** ... ... @@ -78,8 +78,6 @@ 78 78 * //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression. 79 79 * //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology. 80 80 * //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET). 81 - 82 - 83 83 ))) 84 84 * ((( 85 85 **Axis 3: Neuroanatomoclinical** ... ... @@ -101,7 +101,6 @@ 101 101 * Access the `/docs` folder for guidelines. 102 102 * Use `/code` for the latest AI pipelines. 103 103 * Share feedback and ideas in the wiki discussion pages. 104 -* Join the [[Discussion Forum at GitHub>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]] 105 105 106 106 == Key Objectives == 107 107 ... ... @@ -108,13 +108,10 @@ 108 108 * Develop interpretable AI models for diagnosis and progression tracking. 109 109 * Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources. 110 110 * Foster collaboration among neuroscientists, AI researchers, and clinicians. 111 -* Provide a dual diagnostic system: 112 -** Probabilistic Diagnosis – AI assigns multiple traditional possible diagnoses with probability percentages. 113 -** Tridimensional Diagnosis – AI structures diagnoses based on etiology, biomarkers, and neuroanatomical correlations. 114 114 115 115 == Who has access? == 116 116 117 -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!102 +We welcome contributions from the global community. Let’s build the future of neurological diagnostics together! 118 118 ))) 119 119 120 120 ... ... @@ -135,7 +135,6 @@ 135 135 * `/data`: Sample datasets for testing. 136 136 * `/outputs`: Generated models, visualizations, and reports. 137 137 * [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]] 138 -* [[Notebooks>>Notebooks]] 139 139 * [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]] 140 140 * [[to-do-list>>to-do-list]] 141 141 )))