Changes for page Neurodiagnoses

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edited by manuelmenendez
on 2025/02/02 20:53
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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  )))
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17 17  
18 18  = **Overview** =
19 19  
20 -T
21 21  
22 -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.
23 23  
24 -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**.
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 -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**.
27 -
28 -=== **Project Aim** ===
29 -
30 -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**.
31 -
32 32  The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
33 33  
34 34  * **Axis 1**: Etiology (genetic or other causes of diseases).
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41 41  * Integration of incomplete datasets using AI-driven probabilistic modeling.
42 42  * Stratification of patients for personalized treatment.
43 43  
44 -== **The Role of AI-Powered Annotation** ==
37 +== **The role of AI-powered annotation** ==
45 45  
46 -To enhance **standardization, interpretability, and clinical application**, the framework integrates **an AI-powered annotation system**, which:
39 +To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
47 47  
48 -* **Assigns structured metadata tags** to diagnostic features.
49 -* **Provides real-time contextual explanations** for AI-based classifications.
50 -* **Tracks longitudinal disease progression** using timestamped AI annotations.
51 -* **Improves AI model transparency** through interpretability tools (e.g., SHAP analysis).
52 -* **Facilitates decision-making for clinicians** by linking annotations to standardized biomedical ontologies (SNOMED, HPO).
41 +* Assigns structured metadata tags to diagnostic features.
42 +* Provides real-time contextual explanations for AI-based classifications.
43 +* Tracks longitudinal disease progression using timestamped AI annotations.
44 +* Improves AI model transparency through interpretability tools (e.g., SHAP analysis).
45 +* Facilitates decision-making for clinicians by linking annotations to standardized biomedical ontologies (SNOMED, HPO).
53 53  
47 +Neurodiagnoses provides two complementary AI-driven diagnostic approaches:
48 +
49 +1. Traditional Probabilistic Diagnosis
50 +
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.
57 +
58 +2. Tridimensional Diagnosis
59 +
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.
65 +
66 +For every patient case, both systems will be offered, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
67 +
68 +
54 54  == **The case of neurodegenerative diseases** ==
55 55  
56 56  There have been described these 3 diagnostic axes:
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63 63  * //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
64 64  * //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
65 65  * //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
81 +
82 +
66 66  )))
67 67  * (((
68 68  **Axis 2: Molecular Markers**
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70 70  * //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
71 71  * //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
72 72  * //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
90 +
91 +
73 73  )))
74 74  * (((
75 75  **Axis 3: Neuroanatomoclinical**
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97 97  * Develop interpretable AI models for diagnosis and progression tracking.
98 98  * Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
99 99  * Foster collaboration among neuroscientists, AI researchers, and clinicians.
119 +* Provide a dual diagnostic system:
120 +** Probabilistic Diagnosis – AI assigns multiple traditional possible diagnoses with probability percentages.
121 +** Tridimensional Diagnosis – AI structures diagnoses based on etiology, biomarkers, and neuroanatomical correlations.
100 100  
101 101  == Who has access? ==
102 102  
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121 121  * `/data`: Sample datasets for testing.
122 122  * `/outputs`: Generated models, visualizations, and reports.
123 123  * [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]]
146 +* [[Notebooks>>Notebooks]]
124 124  * [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]]
125 125  * [[to-do-list>>to-do-list]]
126 126  )))