Wiki source code of Neurodiagnoses

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5 = //A new tridimensional diagnostic framework for complex CNS diseases// =
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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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16 = What is this about and what can I find here? =
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18 = **Overview** =
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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.
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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.
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25 The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
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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).
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31 This methodology enables:
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33 * Greater precision in diagnosis.
34 * Integration of incomplete datasets using AI-driven probabilistic modeling.
35 * Stratification of patients for personalized treatment.
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37 == **The role of AI-powered annotation** ==
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39 To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
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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).
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47 Neurodiagnoses provides two complementary AI-driven diagnostic approaches:
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49 1. Traditional Probabilistic Diagnosis
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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.
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58 2. Tridimensional Diagnosis
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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.
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66 For every patient case, both systems will be offered, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
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69 == **The case of neurodegenerative diseases** ==
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71 There have been described these 3 diagnostic axes:
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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"]]
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75 * (((
76 **Axis 1: Etiology**
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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.
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84 * (((
85 **Axis 2: Molecular Markers**
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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).
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93 * (((
94 **Axis 3: Neuroanatomoclinical**
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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.
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101 == **Applications** ==
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103 This system enhances:
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105 * **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials.
106 * **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
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108 == How to Contribute ==
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110 * Access the `/docs` folder for guidelines.
111 * Use `/code` for the latest AI pipelines.
112 * Share feedback and ideas in the wiki discussion pages.
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114 == Key Objectives ==
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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 * 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.
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123 == Who has access? ==
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125 We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
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135 {{box title="**Contents**"}}
136 {{toc/}}
137 {{/box}}
138
139 == Main contents ==
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141 * `/docs`: Documentation and contribution guidelines.
142 * `/code`: Machine learning pipelines and scripts.
143 * `/data`: Sample datasets for testing.
144 * `/outputs`: Generated models, visualizations, and reports.
145 * [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]]
146 * [[Notebooks>>Notebooks]]
147 * [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]]
148 * [[to-do-list>>to-do-list]]
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