Changes for page Neurodiagnoses

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5 -= //A new tridimensional diagnostic framework for complex CNS diseases// =
5 += Neurodiagnoses =
6 6  
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 -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.
23 +We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
22 22  
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 +== How to Contribute: ==
24 24  
25 -The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
26 -
27 -* **Axis 1**: Etiology (genetic or other causes of diseases).
28 -* **Axis 2**: Molecular Markers (biomarkers).
29 -* **Axis 3**: Neuroanatomoclinical correlations (linking clinical symptoms to structural changes in the nervous system).
30 -
31 -This methodology enables:
32 -
33 -* Greater precision in diagnosis.
34 -* Integration of incomplete datasets using AI-driven probabilistic modeling.
35 -* Stratification of patients for personalized treatment.
36 -
37 -== **The Role of AI-Powered Annotation** ==
38 -
39 -To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
40 -
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).
46 -
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 -
69 -== **The case of neurodegenerative diseases** ==
70 -
71 -There have been described these 3 diagnostic axes:
72 -
73 -[[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"]]
74 -
75 -* (((
76 -**Axis 1: Etiology**
77 -
78 -* //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
79 -* //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
80 -* //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
81 -
82 -
83 -)))
84 -* (((
85 -**Axis 2: Molecular Markers**
86 -
87 -* //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
88 -* //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
89 -* //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
90 -
91 -
92 -)))
93 -* (((
94 -**Axis 3: Neuroanatomoclinical**
95 -
96 -* //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments.
97 -* //Examples//: Hippocampal atrophy correlating with memory deficits.
98 -* //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations.
99 -)))
100 -
101 -== **Applications** ==
102 -
103 -This system enhances:
104 -
105 -* **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials.
106 -* **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
107 -
108 -== How to Contribute ==
109 -
110 110  * Access the `/docs` folder for guidelines.
111 111  * Use `/code` for the latest AI pipelines.
112 112  * Share feedback and ideas in the wiki discussion pages.
113 -
114 -== Key Objectives ==
115 -
116 -* Develop interpretable AI models for diagnosis and progression tracking.
117 -* Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
118 -* Foster collaboration among neuroscientists, AI researchers, and clinicians.
119 -
120 -== Who has access? ==
121 -
122 -We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
123 123  )))
124 124  
125 125  
126 -
127 -
128 -
129 -
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132 132  {{box title="**Contents**"}}
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133 133  {{toc/}}
134 134  {{/box}}
135 135  
136 -== Main contents ==
39 +== Key Objectives: ==
137 137  
138 -* `/docs`: Documentation and contribution guidelines.
139 -* `/code`: Machine learning pipelines and scripts.
140 -* `/data`: Sample datasets for testing.
141 -* `/outputs`: Generated models, visualizations, and reports.
142 -* [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]]
143 -* [[Notebooks>>Notebooks]]
144 -* [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]]
145 -* [[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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