Publications

Authors:

Brusini, Lorenza; Cruciani, Federica; Dall'Aglio, Gabriele; Zajac, Tommaso; Boscolo Galazzo, Ilaria; Zucchelli, Mauro; Menegaz, Gloria

Title:

XAI-Based Assessment of the AMURA Model for Detecting Amyloid-β and Tau Microstructural Signatures in Alzheimer’s Disease

Year:

2024

Type of item:

Articolo in Rivista

Tipologia ANVUR:

Articolo su rivista

Language:

Inglese

Referee:

No

Name of journal:

IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE

ISSN of journal:

2168-2372

N° Volume:

12

Page numbers:

569-579

Keyword:

AMURA; Amiloyd-beta; eXplainable Artificial Intelligence; tau; tract-based spatial statistics

Short description of contents:

Brain microstructural changes already occur in the earliest phases of Alzheimer's disease (AD) as evidenced in diffusion magnetic resonance imaging (dMRI) literature. This study investigates the potential of the novel dMRI Apparent Measures Using Reduced Acquisitions (AMURA) as imaging markers for capturing such tissue modifications. Tract-based spatial statistics (TBSS) and support vector machines (SVMs) based on different measures were exploited to distinguish between amyloid-beta/tau negative (A beta-/tau-) and A beta+/tau+ or A beta+/tau- subjects. Moreover, eXplainable Artificial Intelligence (XAI) was used to highlight the most influential features in the SVMs classifications and to validate the results by seeing the explanations' recurrence across different methods. TBSS analysis revealed significant differences between A beta-/tau- and other groups in line with the literature. The best SVM classification performance reached an accuracy of 0.73 by using advanced measures compared to more standard ones. Moreover, the explainability analysis suggested the results' stability and the central role of the cingulum to show early sign of AD. By relying on SVM classification and XAI interpretation of the outcomes, AMURA indices can be considered viable markers for amyloid and tau pathology. Clinical impact: This pre-clinical research revealed AMURA indices as viable imaging markers for timely AD diagnosis by acquiring clinically feasible dMR images, with advantages compared to more invasive methods employed nowadays.

Product ID:

141048

Handle IRIS:

11562/1136246

Last Modified:

September 19, 2024

Bibliographic citation:

Brusini, Lorenza; Cruciani, Federica; Dall'Aglio, Gabriele; Zajac, Tommaso; Boscolo Galazzo, Ilaria; Zucchelli, Mauro; Menegaz, Gloria, XAI-Based Assessment of the AMURA Model for Detecting Amyloid-β and Tau Microstructural Signatures in Alzheimer’s Disease «IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE» , vol. 12 , 2024 , pp. 569-579

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