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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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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		-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:
	35	+To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
32	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).
	37	+* Assigns structured metadata tags to diagnostic features.
	38	+* Provides real-time contextual explanations for AI-based classifications.
	39	+* Tracks longitudinal disease progression using timestamped AI annotations.
	40	+* Improves AI model transparency through interpretability tools (e.g., SHAP analysis).
	41	+* Facilitates decision-making for clinicians by linking annotations to standardized biomedical ontologies (SNOMED, HPO).
36	36
37		-This methodology enables:
	43	+Neurodiagnoses provides **two complementary AI-driven diagnostic approaches**:
38	38
39		-* Greater precision in diagnosis.
40		-* Integration of incomplete datasets using AI-driven probabilistic modeling.
41		-* Stratification of patients for personalized treatment.
	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.
42	42
43		-== **The Role of AI-Powered Annotation** ==
	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.
44	44
45		-To enhance **standardization, interpretability, and clinical application**, the framework integrates **an AI-powered annotation system**, which:
	56	+Both systems will be offered for every patient case, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
46	46
47		-* **Assigns structured metadata tags** to diagnostic features.
48		-* **Provides real-time contextual explanations** for AI-based classifications.
49		-* **Tracks longitudinal disease progression** using timestamped AI annotations.
50		-* **Improves AI model transparency** through interpretability tools (e.g., SHAP analysis).
51		-* **Facilitates decision-making for clinicians** by linking annotations to standardized biomedical ontologies (SNOMED, HPO).
	58	+== **Disease Prediction and Biomarker Estimation** ==
52	52
	60	+Neurodiagnoses is also implementing **biomarker prediction and disease progression modeling**, using advanced machine learning techniques:
	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	+
	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	+
53	53	== **The case of neurodegenerative diseases** ==
54	54
55	55	There have been described these 3 diagnostic axes:
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58	58
59	59	* (((
60	60	**Axis 1: Etiology**
61		-
62	62	* //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
63	63	* //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
64	64	* //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
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66	66	* (((
67	67	**Axis 2: Molecular Markers**
68		-
69	69	* //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
70	70	* //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
71	71	* //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
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73	73	* (((
74	74	**Axis 3: Neuroanatomoclinical**
75		-
76	76	* //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments.
77	77	* //Examples//: Hippocampal atrophy correlating with memory deficits.
78	78	* //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations.
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82	82
83	83	This system enhances:
84	84
85		-* **Research**: By stratifying patients, reduces cohort heterogeneity in clinical trials.
	100	+* **Research**: By stratifying patients, reducing cohort heterogeneity in clinical trials.
86	86	* **Clinical Practice**: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
87	87
88	88	== How to Contribute ==
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90	90	* Access the `/docs` folder for guidelines.
91	91	* Use `/code` for the latest AI pipelines.
92	92	* 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]]
93	93
94	94	== Key Objectives ==
95	95
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96	96	* Develop interpretable AI models for diagnosis and progression tracking.
97	97	* Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
98	98	* 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.
99	99
100	100	== Who has access? ==
101	101
102		-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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112	112	{{box title="**Contents**"}}
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125	125	* [[to-do-list>>to-do-list]]
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