Volumetric neuroimaging examinations like structural Magnetic Resonance Imaging (sMRI) are routinely applied to support the clinical diagnosis of dementia like Alzheimer's Disease (AD). Neuroradiologists examine 3D sMRI to detect and monitor abnormalities in brain morphology due to AD, like global and/or local brain atrophy and shape alteration of characteristic structures. There is a strong research interest in developing diagnostic systems based on Deep Learning (DL) models to analyse sMRI for AD. However, anatomical information extracted from an sMRI examination needs to be interpreted together with patient's age to distinguish AD patterns from the regular alteration due to a normal ageing process. In this context, part-prototype neural networks integrate the computational advantages of DL in an interpretable-by-design architecture and showed promising results in medical imaging applications. We present PIMPNet, the first interpretable multimodal model for 3D images and demographics applied to the binary classification of AD from 3D sMRI and patient's age. Despite age prototypes do not improve predictive performance compared to the single modality model, this lays the foundation for future work in the direction of the model's design and multimodal prototype training process

翻译：结构磁共振成像（sMRI）等容积神经影像学检查常规应用于支持阿尔茨海默病（AD）等痴呆症的临床诊断。神经放射科医师通过检查3D sMRI来检测和监测由AD引起的脑形态异常，例如整体和/或局部脑萎缩以及特征结构的形状改变。基于深度学习（DL）模型开发用于分析sMRI以诊断AD的系统具有强烈的研究兴趣。然而，从sMRI检查中提取的解剖学信息需要与患者年龄结合解读，以区分AD模式与正常衰老过程导致的常规改变。在此背景下，部分原型神经网络将DL的计算优势整合到可解释性设计的架构中，并在医学影像应用中显示出有前景的结果。我们提出了PIMPNet，这是首个应用于3D sMRI和患者年龄进行AD二分类的可解释多模态模型，处理3D图像和人口统计学数据。尽管与单模态模型相比，年龄原型并未提升预测性能，但这为未来在模型设计和多模态原型训练过程方向的研究奠定了基础。