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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	)))
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	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]]
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