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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 that often fail to capture the complex interplay of pathophysiological mechanisms, molecular biomarkers, and neuroanatomical changes. 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	+**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		-In addition to these clinical diagnostic approaches, Neurodiagnoses has expanded into a research-oriented platform through the integration of **CNS Digital Twins**. This cutting-edge concept involves creating a personalized digital replica of a patient’s CNS by incorporating multi-omics data (proteomics, genomics, lipidomics, transcriptomics), various neuroimaging modalities, and digital health information. These digital twins enable simulations of disease progression, support the discovery of novel biomarkers, and help identify new therapeutic targets.
	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	25	On this page, you will find:
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33	33
34	34	To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
35	35
36		-* Assign structured metadata tags to diagnostic features.
	37	+* Assigns structured metadata tags to diagnostic features.
37	37	* Provides real-time contextual explanations for AI-based classifications.
38	38	* Tracks longitudinal disease progression using timestamped AI annotations.
39	39	* Improves AI model transparency through interpretability tools (e.g., SHAP analysis).
40	40	* Facilitates decision-making for clinicians by linking annotations to standardized biomedical ontologies (SNOMED, HPO).
41	41
42		-Neurodiagnoses provides two complementary AI-driven diagnostic approaches:
	43	+Neurodiagnoses provides **two complementary AI-driven diagnostic approaches**:
43	43
44		-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.
45	45
46		-* AI provides multiple possible diagnoses, each assigned a probability percentage based on biomarker, imaging, and clinical data.
47		-* 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.
48	48
49		-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.
50	50
51		-* Diagnoses are structured based on:
52		-(1) Etiology (genetic, autoimmune, metabolic, infectious)
53		-(2) Molecular Biomarkers (amyloid-beta, tau, inflammatory markers, EEG patterns)
54		-(3) Neuroanatomoclinical Correlations (brain atrophy, connectivity alterations)
55		-* This approach enables precise disease subtyping and biologically meaningful classification, particularly useful for tracking progression over time.
	58	+== **Disease Prediction and Biomarker Estimation** ==
56	56
57		-Both systems will be offered for every patient case, 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:
58	58
	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.
59	59
	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	+
60	60	== **The case of neurodegenerative diseases** ==
61	61
62	62	There have been described these 3 diagnostic axes:
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65	65
66	66	* (((
67	67	**Axis 1: Etiology**
68		-
69	69	* //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
70	70	* //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
71	71	* //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
72		-
73		-
74	74	)))
75	75	* (((
76	76	**Axis 2: Molecular Markers**
77		-
78	78	* //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
79	79	* //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
80	80	* //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
81		-
82		-
83	83	)))
84	84	* (((
85	85	**Axis 3: Neuroanatomoclinical**
86		-
87	87	* //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments.
88	88	* //Examples//: Hippocampal atrophy correlating with memory deficits.
89	89	* //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations.
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93	93
94	94	This system enhances:
95	95
96		-* **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials.
	100	+* **Research**: By stratifying patients, reducing cohort heterogeneity in clinical trials.
97	97	* **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
98	98
99	99	== How to Contribute ==
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101	101	* Access the `/docs` folder for guidelines.
102	102	* Use `/code` for the latest AI pipelines.
103	103	* Share feedback and ideas in the wiki discussion pages.
104		-* Join the [[Discussion Forum at GitHub>>https://github.com/manuelmenendezgonzalez/neurodiagnoses/discussions]]
	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]]
105	105
106	106	== Key Objectives ==
107	107
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109	109	* Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
110	110	* Foster collaboration among neuroscientists, AI researchers, and clinicians.
111	111	* Provide a dual diagnostic system:
112		-** Probabilistic Diagnosis – AI assigns multiple traditional possible diagnoses with probability percentages.
113		-** Tridimensional Diagnosis – AI structures diagnoses based on etiology, biomarkers, and neuroanatomical correlations.
	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.
114	114
115	115	== Who has access? ==
116	116
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117	117	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!
118	118	)))
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120		-
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125	125	(% class="col-xs-12 col-sm-4" %)
126	126	(((
127	127	{{box title="**Contents**"}}
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140	140	* [[to-do-list>>to-do-list]]
141	141	)))
142	142	)))
	145	+