Changes for page Neurodiagnoses

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edited by manuelmenendez
on 2025/03/03 22:46
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5 -= //A new tridimensional diagnostic framework for CNS diseases// =
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 CNS diseases by using advanced AI techniques and integrating data from neuroimaging, biomarkers, and biomedical ontologies.
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 8  We aim to create a structured, interpretable, and scalable diagnostic tool.
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10 10  )))
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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.
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. Neurodegenerative and psychiatric disorders, for example, exhibit significant clinical overlap, co-pathology, and heterogeneity, making current diagnostic models insufficient.
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.
22 22  
23 -This project proposes a new diagnostic framework—one that shifts from symptom-based classifications to an etiology-driven, tridimensional system. By integrating genetics, proteomics, neuroimaging, computational modeling, and AI-powered annotations, this approach aims to provide a more precise, scalable, and biologically grounded method for diagnosing and managing CNS diseases.
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.
24 24  
25 -The AI-powered annotation system plays a critical role by structuring, interpreting, and tracking multi-modal data, ensuring real-time disease progression analysis, clinician decision support, and personalized treatment pathways.
26 +On this page, you will find:
26 26  
27 -=== **Project Aim** ===
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.
28 28  
29 -Neurodiagnoses is an open-source AI-powered diagnostic system designed for complex central nervous system (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.
33 +== **The role of AI-powered annotation** ==
30 30  
31 -The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
32 -
33 -* **Axis 1**: Etiology (genetic or other causes of diseases).
34 -* **Axis 2**: Molecular Markers (biomarkers).
35 -* **Axis 3**: Neuroanatomoclinical correlations (linking clinical symptoms to structural changes in the nervous system).
36 -
37 -This methodology enables:
38 -
39 -* Greater precision in diagnosis.
40 -* Integration of incomplete datasets using AI-driven probabilistic modeling.
41 -* Stratification of patients for personalized treatment.
42 -
43 -== **The Role of AI-Powered Annotation** ==
44 -
45 45  To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
46 46  
47 47  * Assigns structured metadata tags to diagnostic features.
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50 50  * Improves AI model transparency through interpretability tools (e.g., SHAP analysis).
51 51  * Facilitates decision-making for clinicians by linking annotations to standardized biomedical ontologies (SNOMED, HPO).
52 52  
53 -Neurodiagnoses provides two complementary AI-driven diagnostic approaches:
43 +Neurodiagnoses provides **two complementary AI-driven diagnostic approaches**:
54 54  
55 -1. Traditional Probabilistic Diagnosis
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.
56 56  
57 -* AI provides multiple possible diagnoses, each assigned a probability percentage based on biomarker, imaging, and clinical data.
58 -* Example Output:
59 -** 75% Alzheimer's Disease
60 -** 20% Lewy Body Dementia
61 -** 5% Vascular Dementia
62 -* Useful for differential diagnosis and treatment decision-making.
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.
63 63  
64 -2. Tridimensional Diagnosis
56 +Both systems will be offered for every patient case, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
65 65  
66 -* Diagnoses are structured based on:
67 -(1) Etiology (genetic, autoimmune, metabolic, infectious)
68 -(2) Molecular Biomarkers (amyloid-beta, tau, inflammatory markers, EEG patterns)
69 -(3) Neuroanatomoclinical Correlations (brain atrophy, connectivity alterations)
70 -* This approach enables precise disease subtyping and biologically meaningful classification, particularly useful to track progression over time.
58 +== **Disease Prediction and Biomarker Estimation** ==
71 71  
72 -For every patient case, both systems will be offered, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
60 +Neurodiagnoses is also implementing **biomarker prediction and disease progression modeling**, using advanced machine learning techniques:
73 73  
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.
74 74  
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 +
75 75  == **The case of neurodegenerative diseases** ==
76 76  
77 77  There have been described these 3 diagnostic axes:
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80 80  
81 81  * (((
82 82  **Axis 1: Etiology**
83 -
84 84  * //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
85 85  * //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
86 86  * //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
87 -
88 -
89 89  )))
90 90  * (((
91 91  **Axis 2: Molecular Markers**
92 -
93 93  * //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
94 94  * //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
95 95  * //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
96 -
97 -
98 98  )))
99 99  * (((
100 100  **Axis 3: Neuroanatomoclinical**
101 -
102 102  * //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments.
103 103  * //Examples//: Hippocampal atrophy correlating with memory deficits.
104 104  * //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations.
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108 108  
109 109  This system enhances:
110 110  
111 -* **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials.
100 +* **Research**: By stratifying patients, reducing cohort heterogeneity in clinical trials.
112 112  * **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
113 113  
114 114  == How to Contribute ==
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116 116  * Access the `/docs` folder for guidelines.
117 117  * Use `/code` for the latest AI pipelines.
118 118  * 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]]
119 119  
120 120  == Key Objectives ==
121 121  
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122 122  * Develop interpretable AI models for diagnosis and progression tracking.
123 123  * Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
124 124  * 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.
125 125  
126 126  == Who has access? ==
127 127  
128 -We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
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!
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138 138  {{box title="**Contents**"}}
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151 151  * [[to-do-list>>to-do-list]]
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153 153  )))
145 +