Wiki source code of Neurodiagnoses

Version 34.1 by manuelmenendez on 2025/02/01 13:54

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23.1	5	= //A new tridimensional diagnostic framework for CNS diseases// =
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23.1	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.
6.1	8	We aim to create a structured, interpretable, and scalable diagnostic tool.
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6.1	16	= What is this about and what can I find here? =
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27.1	18	= Overview =
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33.1	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.
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33.1	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.
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	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.
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	27	=== Project Aim ===
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	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.
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23.1	31	The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
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16.1	33	* Axis 1: Etiology (genetic or other causes of diseases).
	34	* Axis 2: Molecular Markers (biomarkers).
6.1	35	* Axis 3: Neuroanatomoclinical correlations (linking clinical symptoms to structural changes in the nervous system).
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	37	This methodology enables:
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	39	* Greater precision in diagnosis.
	40	* Integration of incomplete datasets using AI-driven probabilistic modeling.
	41	* Stratification of patients for personalized treatment.
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33.1	43	== The Role of AI-Powered Annotation ==
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	45	To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
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	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).
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23.1	53	== The case of neurodegenerative diseases ==
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23.1	55	There have been described these 3 diagnostic axes:
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25.1	57	[[Neurodegenerative diseases can be studied and classified in a tridimensional scheme with three axes: anatomic–clinical, molecular, and etiologic. CSF, cerebrospinal fluid; FDG, fluorodeoxyglucose; MRI, magnetic resonance imaging; PET, positron emission tomography.>>image:tridimensional.png\|\|alt="tridimensional view of neurodegenerative diseases"]]
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6.1	59	* (((
	60	Axis 1: Etiology
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	62	* //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
	63	* //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
23.1	64	* //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
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	66	* (((
	67	Axis 2: Molecular Markers
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	69	* //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
	70	* //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
	71	* //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
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	73	* (((
	74	Axis 3: Neuroanatomoclinical
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	76	* //Description//: Links clinical symptoms to neuroanatomical changes, such as atrophy or functional impairments.
	77	* //Examples//: Hippocampal atrophy correlating with memory deficits.
	78	* //Tests//: MRI volumetrics, FDG-PET, neuropsychological evaluations.
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9.1	81	== Applications ==
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	83	This system enhances:
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7.2	85	* Research: By stratifying patients, reduces cohort heterogeneity in clinical trials.
6.1	86	* Clinical Practice: Provides dynamic diagnostic annotations with timestamps for longitudinal tracking.
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10.1	88	== How to Contribute ==
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	90	* Access the `/docs` folder for guidelines.
	91	* Use `/code` for the latest AI pipelines.
	92	* Share feedback and ideas in the wiki discussion pages.
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10.1	94	== Key Objectives ==
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	96	* Develop interpretable AI models for diagnosis and progression tracking.
	97	* Integrate data from Human Phenotype Ontology (HPO), Gene Ontology (GO), and other biomedical resources.
	98	* Foster collaboration among neuroscientists, AI researchers, and clinicians.
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	100	== Who has access? ==
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	102	We welcome contributions from the global community. Let’s build the future of neurological diagnostics together!
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	112	{{box title="Contents"}}
	113	{{toc/}}
	114	{{/box}}
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10.1	116	== Main contents ==
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5.1	118	* `/docs`: Documentation and contribution guidelines.
	119	* `/code`: Machine learning pipelines and scripts.
	120	* `/data`: Sample datasets for testing.
	121	* `/outputs`: Generated models, visualizations, and reports.
21.1	122	* [[Methodology>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Methodology/]]
	123	* [[Results>>url:https://wiki.ebrains.eu/bin/view/Collabs/neurodiagnoses/Results/]]
22.1	124	* [[to-do-list>>to-do-list]]
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