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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.
9	9	)))
10	10	)))
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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.
	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	22
23		-Neurodiagnoses is an open-source AI-powered diagnostic system designed for complex 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.
	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	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	+
	27	+=== **Project Aim** ===
	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**.
	30	+
25	25	The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
26	26
27	27	* **Axis 1**: Etiology (genetic or other causes of diseases).
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36	36
37	37	== **The Role of AI-Powered Annotation** ==
38	38
39		-To enhance standardization, interpretability, and clinical application, the framework integrates an AI-powered annotation system, which:
	45	+To enhance **standardization, interpretability, and clinical application**, the framework integrates **an AI-powered annotation system**, which:
40	40
41		-* Assigns structured metadata tags to diagnostic features.
42		-* Provides real-time contextual explanations for AI-based classifications.
43		-* Tracks longitudinal disease progression using timestamped AI annotations.
44		-* Improves AI model transparency through interpretability tools (e.g., SHAP analysis).
45		-* Facilitates decision-making for clinicians by linking annotations to standardized biomedical ontologies (SNOMED, HPO).
	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).
46	46
47		-Neurodiagnoses provides two complementary AI-driven diagnostic approaches:
48		-
49		-1. Traditional Probabilistic Diagnosis
50		-
51		-* AI provides multiple possible diagnoses, each assigned a probability percentage based on biomarker, imaging, and clinical data.
52		-* Example Output:
53		-** 75% Alzheimer's Disease
54		-** 20% Lewy Body Dementia
55		-** 5% Vascular Dementia
56		-* Useful for differential diagnosis and treatment decision-making.
57		-
58		-2. Tridimensional Diagnosis
59		-
60		-* Diagnoses are structured based on:
61		-(1) Etiology (genetic, autoimmune, metabolic, infectious)
62		-(2) Molecular Biomarkers (amyloid-beta, tau, inflammatory markers, EEG patterns)
63		-(3) Neuroanatomoclinical Correlations (brain atrophy, connectivity alterations)
64		-* This approach enables precise disease subtyping and biologically meaningful classification, particularly useful to track progression over time.
65		-
66		-For every patient case, both systems will be offered, allowing clinicians to compare AI-generated probabilistic diagnosis with a structured tridimensional classification.
67		-
68		-
69	69	== **The case of neurodegenerative diseases** ==
70	70
71	71	There have been described these 3 diagnostic axes:
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78	78	* //Description//: Focuses on genetic and sporadic causes, identifying risk factors and potential triggers.
79	79	* //Examples//: APOE ε4 as a genetic risk factor, or cardiovascular health affecting NDD progression.
80	80	* //Tests//: Genetic testing, lifestyle, and cardiovascular screening.
81		-
82		-
83	83	)))
84	84	* (((
85	85	**Axis 2: Molecular Markers**
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87	87	* //Description//: Analyzes primary (amyloid-beta, tau) and secondary biomarkers (NFL, GFAP) for tracking disease progression.
88	88	* //Examples//: CSF amyloid-beta concentrations to confirm Alzheimer’s pathology.
89	89	* //Tests//: Blood/CSF biomarkers, PET imaging (Tau-PET, Amyloid-PET).
90		-
91		-
92	92	)))
93	93	* (((
94	94	**Axis 3: Neuroanatomoclinical**