发表机构
Indian Institute of Technology Indore; Indian Institute of Technology Patna; M. R. Bangur Hospital; University of Western Australia（印度理工学院印多尔分校; 印度理工学院巴特那分校; M. R. 班古尔医院; 西澳大利亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出可解释框架，结合视觉建模与临床推理，从手绘模式中提取运动描述符，实现帕金森病筛查并生成临床依据。
AI 中文摘要
帕金森病（PD）早期表现为神经运动障碍，这些障碍在受控的手绘模式（如螺旋线和蜿蜒线）中变得可观察，其中震颤引起的振荡、笔画不规则性和曲率不稳定性反映了潜在的运动退化。在本工作中，我们提出了一种用于离线手绘模式PD筛查的可解释框架，该框架将判别性视觉建模与临床推理相结合。预测模型捕捉分布式的结构畸变和细粒度的纹理变化，并在受试者不相交的协议下进行评估，以确保可靠的泛化能力。为了超越黑箱分类，我们引入了一个多阶段可解释性流水线，将视觉归因与结构化症状抽象相结合。首先使用基于注意力和梯度的定位方法识别显著区域，然后提取具有临床意义的运动描述符，量化轮廓粗糙度、曲率不规则性、笔画变异性和震颤频率能量。这些描述符随后通过基于语言的推理模块转化为连贯的临床依据，将模型证据与既定的PD症状学联系起来。通过弥合视觉归因与临床解释之间的差距，所提出的框架推进了使用手绘模式进行神经筛查的可解释文档智能。在公开可用的帕金森病手写数据集上的实验结果表明，该框架具有竞争力的预测性能和临床一致的解释。
英文摘要
Parkinson's disease (PD) manifests early neuromotor impairments that become observable in controlled hand-drawn patterns such as spirals and meanders, where tremor-induced oscillations, stroke irregularity, and curvature instability reflect underlying motor degradation. In this work, we present an explainable framework for PD screening from offline hand-drawn patterns that integrates discriminative visual modeling with clinically grounded reasoning. The predictive model captures distributed structural distortions and fine-grained texture variations. It is evaluated under subject-disjoint protocols to ensure reliable generalization. To move beyond black-box classification, we introduce a multi-stage explainability pipeline that combines visual attribution with structured symptom abstraction. Salient regions are identified using attention- and gradient-based localization, followed by extraction of clinically meaningful motor descriptors quantifying contour roughness, curvature irregularity, stroke variability, and tremor-frequency energy. These descriptors are subsequently translated into coherent clinical rationales through a language-based reasoning module, linking model evidence to established PD symptomatology. By bridging visual attribution and clinical interpretation, the proposed framework advances interpretable document intelligence for neurological screening using hand-drawn patterns. Experimental results on publicly available Parkinson's disease handwriting datasets demonstrate competitive predictive performance and clinically consistent explanations.
