Changes for page Neurodiagnoses

Last modified by manuelmenendez on 2025/03/03 22:46

From version 47.1
edited by manuelmenendez
on 2025/03/03 22:46
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edited by manuelmenendez
on 2025/02/02 15:12
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17 17  
18 18  = **Overview** =
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 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.
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 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.
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 25  
26 -On this page, you will find:
25 +The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
27 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.
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).
32 32  
33 -== **The role of AI-powered annotation** ==
31 +This methodology enables:
34 34  
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 +
35 35  To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
36 36  
37 37  * Assigns structured metadata tags to diagnostic features.
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40 40  * Improves AI model transparency through interpretability tools (e.g., SHAP analysis).
41 41  * Facilitates decision-making for clinicians by linking annotations to standardized biomedical ontologies (SNOMED, HPO).
42 42  
43 -Neurodiagnoses provides **two complementary AI-driven diagnostic approaches**:
47 +Neurodiagnoses provides two complementary AI-driven diagnostic approaches:
44 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.
49 +1. Traditional Probabilistic Diagnosis
48 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.
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.
55 55  
56 -Both systems will be offered for every patient case, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
58 +2. Tridimensional Diagnosis
57 57  
58 -== **Disease Prediction and Biomarker Estimation** ==
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.
59 59  
60 -Neurodiagnoses is also implementing **biomarker prediction and disease progression modeling**, using advanced machine learning techniques:
66 +For every patient case, both systems will be offered, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
61 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 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 71  == **The case of neurodegenerative diseases** ==
72 72  
73 73  There have been described these 3 diagnostic axes:
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76 76  
77 77  * (((
78 78  **Axis 1: Etiology**
77 +
79 79  * //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
80 80  * //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
81 81  * //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
81 +
82 +
82 82  )))
83 83  * (((
84 84  **Axis 2: Molecular Markers**
86 +
85 85  * //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
86 86  * //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
87 87  * //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
90 +
91 +
88 88  )))
89 89  * (((
90 90  **Axis 3: Neuroanatomoclinical**
95 +
91 91  * //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments.
92 92  * //Examples//: Hippocampal atrophy correlating with memory deficits.
93 93  * //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations.
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97 97  
98 98  This system enhances:
99 99  
100 -* **Research**: By stratifying patients, reducing cohort heterogeneity in clinical trials.
105 +* **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials.
101 101  * **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
102 102  
103 103  == How to Contribute ==
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105 105  * Access the `/docs` folder for guidelines.
106 106  * Use `/code` for the latest AI pipelines.
107 107  * 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]]
110 110  
111 111  == Key Objectives ==
112 112  
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113 113  * Develop interpretable AI models for diagnosis and progression tracking.
114 114  * Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
115 115  * 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.
121 121  
122 122  == Who has access? ==
123 123  
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!
122 +We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
125 125  )))
126 126  
125 +
126 +
127 +
128 +
129 +
127 127  (% class="col-xs-12 col-sm-4" %)
128 128  (((
129 129  {{box title="**Contents**"}}
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142 142  * [[to-do-list>>to-do-list]]
143 143  )))
144 144  )))
145 -