Changes for page Neurodiagnoses
Last modified by manuelmenendez on 2025/03/03 22:46
From version 37.1
edited by manuelmenendez
on 2025/02/02 07:14
on 2025/02/02 07:14
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To version 41.1
edited by manuelmenendez
on 2025/02/02 15:13
on 2025/02/02 15:13
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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 ))) ... ... @@ -18,16 +18,10 @@ 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 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.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. 22 22 23 - This project proposes a new diagnosticframework—onethat shifts from symptom-based classificationsto antiology-driven,tridimensionalsystem.Byintegratinggenetics,proteomics,neuroimaging,computationalmodeling,andAI-powered annotations,this approach aimstoprovidemoreprecise,scalable, andbiologicallygrounded methodfordiagnosingandmanagingCNS diseases.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. 24 24 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 - 27 -=== **Project Aim** === 28 - 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. 30 - 31 31 The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on: 32 32 33 33 * **Axis 1**: Etiology (genetic or other causes of diseases). ... ... @@ -40,7 +40,7 @@ 40 40 * Integration of incomplete datasets using AI-driven probabilistic modeling. 41 41 * Stratification of patients for personalized treatment. 42 42 43 -== **The Role of AI-PoweredAnnotation** ==37 +== **The role of AI-powered annotation** == 44 44 45 45 To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which: 46 46 ... ... @@ -56,11 +56,9 @@ 56 56 57 57 * AI provides multiple possible diagnoses, each assigned a probability percentage based on biomarker, imaging, and clinical data. 58 58 * Example Output: 59 - 60 -{{{75% Alzheimer's Disease 61 -20% Lewy Body Dementia 62 -5% Vascular Dementia 63 -}}} 53 +** 75% Alzheimer's Disease 54 +** 20% Lewy Body Dementia 55 +** 5% Vascular Dementia 64 64 * Useful for differential diagnosis and treatment decision-making. 65 65 66 66 2. Tridimensional Diagnosis ... ... @@ -69,7 +69,7 @@ 69 69 (1) Etiology (genetic, autoimmune, metabolic, infectious) 70 70 (2) Molecular Biomarkers (amyloid-beta, tau, inflammatory markers, EEG patterns) 71 71 (3) Neuroanatomoclinical Correlations (brain atrophy, connectivity alterations) 72 -* This approach enables precise disease subtyping and biologically meaningful classification. 64 +* This approach enables precise disease subtyping and biologically meaningful classification, particularly useful to track progression over time. 73 73 74 74 For every patient case, both systems will be offered, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification. 75 75