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5		-= //A new tridimensional diagnostic framework for complex CNS diseases// =
	5	+= //A new tridimensional diagnostic framework for CNS diseases// =
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.
	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.
8	8	We aim to create a structured, interpretable, and scalable diagnostic tool.
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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. Neurodegenerative and psychiatric disorders, for example, exhibit significant clinical overlap, co-pathology, and heterogeneity, making current diagnostic models insufficient.
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	+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.
25	25
26		-On this page, you will find:
	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.
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	+=== **Project Aim** ===
32	32
33		-== **The role of AI-powered annotation** ==
	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.
34	34
	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	+
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**:
	53	+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.
	55	+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.
	57	+* AI provides multiple possible diagnoses, each assigned a probability percentage based on biomarker, imaging, and clinical data.
	58	+* Example Output:
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.
	60	+>
57	57
58		-== **Disease Prediction and Biomarker Estimation** ==
	62	+{{{75% Alzheimer's Disease
	63	+20% Lewy Body Dementia
	64	+5% Vascular Dementia
	65	+}}}
59	59
60		-Neurodiagnoses is also implementing **biomarker prediction and disease progression modeling**, using advanced machine learning techniques:
	67	+* Useful for differential diagnosis and treatment decision-making.
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.
	69	+2. Tridimensional Diagnosis
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.
	71	+* Diagnoses are structured based on:
	72	+(1) Etiology (genetic, autoimmune, metabolic, infectious)
	73	+(2) Molecular Biomarkers (amyloid-beta, tau, inflammatory markers, EEG patterns)
	74	+(3) Neuroanatomoclinical Correlations (brain atrophy, connectivity alterations)
	75	+* This approach enables precise disease subtyping and biologically meaningful classification.
70	70
	77	+For every patient case, both systems will be offered, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
	78	+
	79	+
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**
	88	+
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.
	92	+
	93	+
82	82	)))
83	83	* (((
84	84	**Axis 2: Molecular Markers**
	97	+
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).
	101	+
	102	+
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89	89	* (((
90	90	**Axis 3: Neuroanatomoclinical**
	106	+
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.
	116	+* **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!
	133	+We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
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129	129	{{box title="**Contents**"}}
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142	142	* [[to-do-list>>to-do-list]]
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