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

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL
Lorenza Brusini;Federica Cruciani;Gabriele Dall’Aglio;Tommaso Zajac;Ilaria Boscolo Galazzo;Mauro Zucchelli;Gloria Menegaz
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Abstract

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.
基于 XAI 的 AMURA 模型对检测阿尔茨海默病淀粉样蛋白-β 和 Tau 微结构特征的评估
正如弥散磁共振成像(dMRI)文献所证实的那样,阿尔茨海默病(AD)的早期阶段已经出现大脑微结构变化。本研究探讨了新型 dMRI 表观测量(Apparent Measures Using Reduced Acquisitions,AMURA)作为成像标记捕捉此类组织变化的潜力。研究人员利用基于不同测量方法的肽段空间统计(Tract-based spatial statistics,TBSS)和支持向量机(Support vector machines,SVMs)来区分淀粉样蛋白-β/tau 阴性(A $\beta $ -/tau-)和 A $\beta $ +/tau+ 或 A $\beta $ +/tau- 受试者。此外,eXplainable 人工智能(XAI)被用来突出 SVMs 分类中最有影响力的特征,并通过查看不同方法中解释的重复性来验证结果。与更标准的方法相比,使用高级方法的 SVM 分类准确率达到了 0.73。此外,可解释性分析表明了结果的稳定性以及蝶鞍在显示 AD 早期迹象方面的核心作用。通过依赖 SVM 分类和 XAI 结果解释,AMURA 指数可被视为淀粉样蛋白和 tau 病理学的可行标记。临床影响:这项临床前研究通过获取临床上可行的dMR图像,揭示了AMURA指数是及时诊断AD的可行成像标记物,与目前采用的更具侵入性的方法相比具有优势。
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来源期刊
CiteScore
7.40
自引率
2.90%
发文量
65
审稿时长
27 weeks
期刊介绍: The IEEE Journal of Translational Engineering in Health and Medicine is an open access product that bridges the engineering and clinical worlds, focusing on detailed descriptions of advanced technical solutions to a clinical need along with clinical results and healthcare relevance. The journal provides a platform for state-of-the-art technology directions in the interdisciplinary field of biomedical engineering, embracing engineering, life sciences and medicine. A unique aspect of the journal is its ability to foster a collaboration between physicians and engineers for presenting broad and compelling real world technological and engineering solutions that can be implemented in the interest of improving quality of patient care and treatment outcomes, thereby reducing costs and improving efficiency. The journal provides an active forum for clinical research and relevant state-of the-art technology for members of all the IEEE societies that have an interest in biomedical engineering as well as reaching out directly to physicians and the medical community through the American Medical Association (AMA) and other clinical societies. The scope of the journal includes, but is not limited, to topics on: Medical devices, healthcare delivery systems, global healthcare initiatives, and ICT based services; Technological relevance to healthcare cost reduction; Technology affecting healthcare management, decision-making, and policy; Advanced technical work that is applied to solving specific clinical needs.
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