Changes for page Neurodiagnoses
Last modified by manuelmenendez on 2025/03/03 22:46
From version 43.1
edited by manuelmenendez
on 2025/02/05 11:14
on 2025/02/05 11:14
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To version 37.1
edited by manuelmenendez
on 2025/02/02 07:14
on 2025/02/02 07:14
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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 ))) ... ... @@ -18,23 +18,33 @@ 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 integration of**CNS Digital Twins**.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 29 +Neurodiagnoses is 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. 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. 32 32 33 - == **The roleofAI-powered annotation**==31 +The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on: 34 34 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 + 35 35 To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which: 36 36 37 -* Assign structured metadata tags to diagnostic features. 47 +* Assigns structured metadata tags to diagnostic features. 38 38 * Provides real-time contextual explanations for AI-based classifications. 39 39 * Tracks longitudinal disease progression using timestamped AI annotations. 40 40 * Improves AI model transparency through interpretability tools (e.g., SHAP analysis). ... ... @@ -45,6 +45,12 @@ 45 45 1. Traditional Probabilistic Diagnosis 46 46 47 47 * AI provides multiple possible diagnoses, each assigned a probability percentage based on biomarker, imaging, and clinical data. 58 +* Example Output: 59 + 60 +{{{75% Alzheimer's Disease 61 +20% Lewy Body Dementia 62 +5% Vascular Dementia 63 +}}} 48 48 * Useful for differential diagnosis and treatment decision-making. 49 49 50 50 2. Tridimensional Diagnosis ... ... @@ -53,9 +53,9 @@ 53 53 (1) Etiology (genetic, autoimmune, metabolic, infectious) 54 54 (2) Molecular Biomarkers (amyloid-beta, tau, inflammatory markers, EEG patterns) 55 55 (3) Neuroanatomoclinical Correlations (brain atrophy, connectivity alterations) 56 -* This approach enables precise disease subtyping and biologically meaningful classification , particularly useful for tracking progression over time.72 +* This approach enables precise disease subtyping and biologically meaningful classification. 57 57 58 - Both systems will be offeredfor every patient case, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.74 +For every patient case, both systems will be offered, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification. 59 59 60 60 61 61 == **The case of neurodegenerative diseases** == ... ... @@ -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!130 +We welcome contributions from the global community. Let’s build the future of neurological diagnostics together! 118 118 ))) 119 119 120 120