Changes for page Neurodiagnoses

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5 -= //A new tridimensional diagnostic framework for CNS diseases// =
5 += Neurodiagnoses =
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.
8 -We aim to create a structured, interpretable, and scalable diagnostic tool.
7 +This project is focused on developing a novel nosological and diagnostic framework for neurological diseases. Using advanced AI techniques and integrating data from neuroimaging, biomarkers, and biomedical ontologies, we aim to create a structured, interpretable, and scalable diagnostic tool.
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11 11  
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16 -= What is this about and what can I find here? =
15 += What can I find here? =
17 17  
18 -= **Overview** =
17 +* `/docs`: Documentation and contribution guidelines.
18 +* `/code`: Machine learning pipelines and scripts.
19 +* `/data`: Sample datasets for testing. - `/outputs`: Generated models, visualizations, and reports.
19 19  
21 += Who has access? =
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.
23 +We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
22 22  
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.
25 +== How to Contribute: ==
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 -The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
32 -
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 -
45 -To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
46 -
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).
52 -
53 -Neurodiagnoses provides two complementary AI-driven diagnostic approaches:
54 -
55 -1. Traditional Probabilistic Diagnosis
56 -
57 -* AI provides multiple possible diagnoses, each assigned a probability percentage based on biomarker, imaging, and clinical data.
58 -* Example Output:
59 -
60 ->
61 -
62 -{{{75% Alzheimer's Disease
63 -20% Lewy Body Dementia
64 -5% Vascular Dementia
65 -}}}
66 -
67 -* Useful for differential diagnosis and treatment decision-making.
68 -
69 -2. Tridimensional Diagnosis
70 -
71 -* Diagnoses are structured based on:
72 -(1) Etiology (genetic, autoimmune, metabolic, infectious)
73 -(2) Molecular Biomarkers (amyloid-beta, tau, inflammatory markers, EEG patterns)
74 -(3) Neuroanatomoclinical Correlations (brain atrophy, connectivity alterations)
75 -* This approach enables precise disease subtyping and biologically meaningful classification.
76 -
77 -For every patient case, both systems will be offered, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
78 -
79 -
80 -== **The case of neurodegenerative diseases** ==
81 -
82 -There have been described these 3 diagnostic axes:
83 -
84 -[[Neurodegenerative diseases can be studied and classified in a tridimensional scheme with three axes: anatomic–clinical, molecular, and etiologic. CSF, cerebrospinal fluid; FDG, fluorodeoxyglucose; MRI, magnetic resonance imaging; PET, positron emission tomography.>>image:tridimensional.png||alt="tridimensional view of neurodegenerative diseases"]]
85 -
86 -* (((
87 -**Axis 1: Etiology**
88 -
89 -* //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
90 -* //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
91 -* //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
92 -
93 -
94 -)))
95 -* (((
96 -**Axis 2: Molecular Markers**
97 -
98 -* //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
99 -* //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
100 -* //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
101 -
102 -
103 -)))
104 -* (((
105 -**Axis 3: Neuroanatomoclinical**
106 -
107 -* //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments.
108 -* //Examples//: Hippocampal atrophy correlating with memory deficits.
109 -* //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations.
110 -)))
111 -
112 -== **Applications** ==
113 -
114 -This system enhances:
115 -
116 -* **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials.
117 -* **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
118 -
119 -== How to Contribute ==
120 -
121 121  * Access the `/docs` folder for guidelines.
122 122  * Use `/code` for the latest AI pipelines.
123 123  * Share feedback and ideas in the wiki discussion pages.
124 -
125 -== Key Objectives ==
126 -
127 -* Develop interpretable AI models for diagnosis and progression tracking.
128 -* Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
129 -* Foster collaboration among neuroscientists, AI researchers, and clinicians.
130 -
131 -== Who has access? ==
132 -
133 -We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
134 134  )))
135 135  
136 136  
137 -
138 -
139 -
140 -
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143 143  {{box title="**Contents**"}}
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144 144  {{toc/}}
145 145  {{/box}}
146 146  
147 -== Main contents ==
39 +== Key Objectives: ==
148 148  
149 -* `/docs`: Documentation and contribution guidelines.
150 -* `/code`: Machine learning pipelines and scripts.
151 -* `/data`: Sample datasets for testing.
152 -* `/outputs`: Generated models, visualizations, and reports.
153 -* [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]]
154 -* [[Notebooks>>Notebooks]]
155 -* [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]]
156 -* [[to-do-list>>to-do-list]]
41 +* Develop interpretable AI models for diagnosis and progression tracking.
42 +* Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
43 +* Foster collaboration among neuroscientists, AI researchers, and clinicians.
44 +
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158 158  )))
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