Changes for page Neurodiagnoses

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5 -= Neurodiagnoses =
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 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.
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.
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10 10  
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15 -= What can I find here? =
16 += What is this about and what can I find here? =
16 16  
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.
18 += **Overview** =
20 20  
21 -= Who has access? =
22 22  
23 -We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
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.
24 24  
25 -== How to Contribute: ==
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.
26 26  
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 +
27 27  * Access the `/docs` folder for guidelines.
28 28  * Use `/code` for the latest AI pipelines.
29 29  * 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!
30 30  )))
31 31  
32 32  
126 +
127 +
128 +
129 +
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35 35  {{box title="**Contents**"}}
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36 36  {{toc/}}
37 37  {{/box}}
38 38  
39 -== Key Objectives: ==
136 +== Main contents ==
40 40  
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 -
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]]
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