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  
20 -The classification and diagnosis of central nervous system (CNS) diseases have long been constrained by traditional, phenotype-based approaches that often fail to capture the complex interplay of pathophysiological mechanisms, molecular biomarkers, and neuroanatomical changes.
21 += Who has access? =
21 21  
22 -**Neurodiagnoses** redefines this landscape by integrating advanced AI with multi-modal data—including genetics, neuroimaging, biomarkers, and digital health records—to create a more precise, scalable, and data-driven diagnostic system.
23 -
24 -Additionally, **Neurodiagnoses is now expanding into disease prediction and biomarker estimation**, integrating state-of-the-art machine learning models to enhance precision diagnostics and disease progression forecasting.
25 -
26 -On this page, you will find:
27 -
28 -* Detailed descriptions of both the clinical diagnostic tools and the research framework.
29 -* Access to our AI models, data processing pipelines, and digital twin simulations.
30 -* Collaborative resources for researchers, clinicians, and AI developers.
31 -* Guidelines and instructions on how to contribute to and expand the project.
32 -
33 -== **The role of AI-powered annotation** ==
34 -
35 -To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
36 -
37 -* Assigns structured metadata tags to diagnostic features.
38 -* Provides real-time contextual explanations for AI-based classifications.
39 -* Tracks longitudinal disease progression using timestamped AI annotations.
40 -* Improves AI model transparency through interpretability tools (e.g., SHAP analysis).
41 -* Facilitates decision-making for clinicians by linking annotations to standardized biomedical ontologies (SNOMED, HPO).
42 -
43 -Neurodiagnoses provides **two complementary AI-driven diagnostic approaches**:
44 -
45 -1. **Probabilistic Diagnosis**
46 - * AI assigns probability scores to multiple possible diagnoses based on biomarker, imaging, and clinical data.
47 - * Useful for differential diagnosis and treatment decision-making.
48 -
49 -2. **Tridimensional Diagnosis**
50 - * Diagnoses are structured based on:
51 - - **(1) Etiology** (genetic, autoimmune, metabolic, infectious).
52 - - **(2) Molecular Biomarkers** (amyloid-beta, tau, inflammatory markers, EEG patterns).
53 - - **(3) Neuroanatomoclinical Correlations** (brain atrophy, connectivity alterations).
54 - * This approach enables precise disease subtyping and biologically meaningful classification, particularly useful for tracking progression over time.
55 -
56 -Both systems will be offered for every patient case, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
57 -
58 -== **Disease Prediction and Biomarker Estimation** ==
59 -
60 -Neurodiagnoses is also implementing **biomarker prediction and disease progression modeling**, using advanced machine learning techniques:
61 -
62 -* **Biomarker Prediction:**
63 - - Estimation of fluid-based and neuroimaging biomarkers without invasive testing.
64 - - Multi-modal machine learning models for predicting molecular and clinical markers.
65 -
66 -* **Disease Progression Modeling:**
67 - - AI-driven forecasts for neurodegenerative disease evolution.
68 - - Probabilistic disease conversion models (e.g., MCI to AD, Parkinson's prodromal phases).
69 - - Survival models and risk stratification for precision medicine applications.
70 -
71 -== **The case of neurodegenerative diseases** ==
72 -
73 -There have been described these 3 diagnostic axes:
74 -
75 -[[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"]]
76 -
77 -* (((
78 -**Axis 1: Etiology**
79 -* //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
80 -* //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
81 -* //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
23 +We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
82 82  )))
83 -* (((
84 -**Axis 2: Molecular Markers**
85 -* //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
86 -* //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
87 -* //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
88 -)))
89 -* (((
90 -**Axis 3: Neuroanatomoclinical**
91 -* //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments.
92 -* //Examples//: Hippocampal atrophy correlating with memory deficits.
93 -* //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations.
94 -)))
95 95  
96 -== **Applications** ==
97 97  
98 -This system enhances:
99 -
100 -* **Research**: By stratifying patients, reducing cohort heterogeneity in clinical trials.
101 -* **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
102 -
103 -== How to Contribute ==
104 -
105 -* Access the `/docs` folder for guidelines.
106 -* Use `/code` for the latest AI pipelines.
107 -* Share feedback and ideas in the wiki discussion pages.
108 -* Join our [[Community on EBRAINS>>https://community.ebrains.eu/_ideas/-OJHTZrpKrrrkx-u0djj/about]]
109 -* Join the [[Discussion Forum at GitHub>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]]
110 -
111 -== Key Objectives ==
112 -
113 -* Develop interpretable AI models for diagnosis and progression tracking.
114 -* Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
115 -* Foster collaboration among neuroscientists, AI researchers, and clinicians.
116 -* Provide a dual diagnostic system:
117 - ** Probabilistic Diagnosis – AI assigns multiple traditional possible diagnoses with probability percentages.
118 - ** Tridimensional Diagnosis – AI structures diagnoses based on etiology, biomarkers, and neuroanatomical correlations.
119 -* Implement disease prediction models for neurodegenerative conditions.
120 -* Predict biomarkers from non-invasive data sources.
121 -
122 -== Who has access? ==
123 -
124 -We welcome contributions from the global community. Join us as we transform CNS diagnostics and drive precision medicine forward through a collaborative, open-source approach. Let’s build the future of neurological diagnostics together!
125 -)))
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129 129  {{box title="**Contents**"}}
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130 130  {{toc/}}
131 131  {{/box}}
132 132  
133 -== Main contents ==
134 -
135 -* `/docs`: Documentation and contribution guidelines.
136 -* `/code`: Machine learning pipelines and scripts.
137 -* `/data`: Sample datasets for testing.
138 -* `/outputs`: Generated models, visualizations, and reports.
139 -* [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]]
140 -* [[Notebooks>>Notebooks]]
141 -* [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]]
142 -* [[to-do-list>>to-do-list]]
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