Summary

• Page properties (1 modified, 0 added, 0 removed)

Details

Page properties

Content

...	...	@@ -2,9 +2,9 @@
2	2	(((
3	3	(% class="container" %)
4	4	(((
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.
9	9	)))
10	10	)))
...	...	@@ -18,33 +18,22 @@
18	18	= **Overview** =
19	19
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.
	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	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.
	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	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.
	25	+On this page, you will find:
26	26
27		-=== **Project Aim** ===
	27	+* Detailed descriptions of both the clinical diagnostic tools and the research framework.
	28	+* Access to our AI models, data processing pipelines, and digital twin simulations.
	29	+* Collaborative resources for researchers, clinicians, and AI developers.
	30	+* 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.
	32	+== **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		-* Assigns structured metadata tags to diagnostic features.
	36	+* Assign structured metadata tags to diagnostic features.
48	48	* Provides real-time contextual explanations for AI-based classifications.
49	49	* Tracks longitudinal disease progression using timestamped AI annotations.
50	50	* Improves AI model transparency through interpretability tools (e.g., SHAP analysis).
...	...	@@ -55,15 +55,6 @@
55	55	1. Traditional Probabilistic Diagnosis
56	56
57	57	* AI provides multiple possible diagnoses, each assigned a probability percentage based on biomarker, imaging, and clinical data.
58		-* Example Output:
59		-
60		->
61		-
62		-{{{75% Alzheimer's Disease
63		-20% Lewy Body Dementia
64		-5% Vascular Dementia
65		-}}}
66		-
67	67	* Useful for differential diagnosis and treatment decision-making.
68	68
69	69	2. Tridimensional Diagnosis
...	...	@@ -72,9 +72,9 @@
72	72	(1) Etiology (genetic, autoimmune, metabolic, infectious)
73	73	(2) Molecular Biomarkers (amyloid-beta, tau, inflammatory markers, EEG patterns)
74	74	(3) Neuroanatomoclinical Correlations (brain atrophy, connectivity alterations)
75		-* This approach enables precise disease subtyping and biologically meaningful classification.
	55	+* This approach enables precise disease subtyping and biologically meaningful classification, particularly useful for tracking progression over time.
76	76
77		-For every patient case, both systems will be offered, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
	57	+Both systems will be offered for every patient case, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
78	78
79	79
80	80	== **The case of neurodegenerative diseases** ==
...	...	@@ -121,6 +121,8 @@
121	121	* Access the `/docs` folder for guidelines.
122	122	* Use `/code` for the latest AI pipelines.
123	123	* Share feedback and ideas in the wiki discussion pages.
	104	+* Join our [[Community on EBRAINS>>https://community.ebrains.eu/_ideas/-OJHTZrpKrrrkx-u0djj/about]]
	105	+* Join the [[Discussion Forum at GitHub>>https://github.com/Fundacion-de-Neurociencias/neurodiagnoses/discussions]]
124	124
125	125	== Key Objectives ==
126	126
...	...	@@ -127,10 +127,13 @@
127	127	* Develop interpretable AI models for diagnosis and progression tracking.
128	128	* Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
129	129	* Foster collaboration among neuroscientists, AI researchers, and clinicians.
	112	+* Provide a dual diagnostic system:
	113	+** Probabilistic Diagnosis – AI assigns multiple traditional possible diagnoses with probability percentages.
	114	+** Tridimensional Diagnosis – AI structures diagnoses based on etiology, biomarkers, and neuroanatomical correlations.
130	130
131	131	== Who has access? ==
132	132
133		-We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
	118	+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!
134	134	)))
135	135
136	136