Summary

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

Details

Page properties

Content

...	...	@@ -18,15 +18,15 @@
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, 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		-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	+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**.
	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	26
27	27	=== **Project Aim** ===
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**.
	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	30
31	31	The //Tridimensional Diagnostic Framework// redefines CNS diseases can be classified and diagnosed by focusing on:
32	32
...	...	@@ -42,13 +42,13 @@
42	42
43	43	== **The Role of AI-Powered Annotation** ==
44	44
45		-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:
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).
	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).
52	52
53	53	== **The case of neurodegenerative diseases** ==
54	54
...	...	@@ -62,6 +62,8 @@
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.
	65	+
	66	+
65	65	)))
66	66	* (((
67	67	**Axis 2: Molecular Markers**
...	...	@@ -69,6 +69,8 @@
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).
	74	+
	75	+
72	72	)))
73	73	* (((
74	74	**Axis 3: Neuroanatomoclinical**