Atypical Parkinsonian Disorders (APD), also known as Parkinson-plus syndrome, are a group of neurodegenerative diseases that include progressive supranuclear palsy (PSP) and multiple system atrophy (MSA). In the early stages, overlapping clinical features often lead to misdiagnosis as Parkinson's disease (PD). Identifying reliable imaging biomarkers for early differential diagnosis remains a critical challenge. In this study, we propose a hybrid framework combining convolutional neural networks (CNNs) with machine learning (ML) techniques to classify APD subtypes versus PD and distinguish between the subtypes themselves: PSP vs. PD, MSA vs. PD, and PSP vs. MSA. The model leverages multi-modal input data, including T1-weighted magnetic resonance imaging (MRI), segmentation masks of 12 deep brain structures associated with APD, and their corresponding volumetric measurements. By integrating these complementary modalities, including image data, structural segmentation masks, and quantitative volume features, the hybrid approach achieved promising classification performance with area under the curve (AUC) scores of 0.95 for PSP vs. PD, 0.86 for MSA vs. PD, and 0.92 for PSP vs. MSA. These results highlight the potential of combining spatial and structural information for robust subtype differentiation. In conclusion, this study demonstrates that fusing CNN-based image features with volume-based ML inputs improves classification accuracy for APD subtypes. The proposed approach may contribute to more reliable early-stage diagnosis, facilitating timely and targeted interventions in clinical practice.

翻译：非典型帕金森病（APD），亦称帕金森叠加综合征，是一组神经退行性疾病，包括进行性核上性麻痹（PSP）和多系统萎缩（MSA）。在疾病早期，重叠的临床症状常导致其被误诊为帕金森病（PD）。寻找可靠的影像学生物标志物以实现早期鉴别诊断仍是一个关键挑战。本研究提出一种结合卷积神经网络（CNN）与机器学习（ML）技术的混合框架，用于对APD亚型与PD进行分类，并区分亚型自身：PSP vs. PD、MSA vs. PD以及PSP vs. MSA。该模型利用多模态输入数据，包括T1加权磁共振成像（MRI）、与APD相关的12个深部脑结构的分割掩膜及其对应的体积测量值。通过整合这些互补模态——包括图像数据、结构分割掩膜和定量体积特征，该混合方法取得了良好的分类性能，其曲线下面积（AUC）得分分别为：PSP vs. PD 0.95、MSA vs. PD 0.86、PSP vs. MSA 0.92。这些结果凸显了结合空间与结构信息对于实现稳健亚型区分的潜力。总之，本研究表明，将基于CNN的图像特征与基于体积的ML输入相融合，能够提高APD亚型的分类准确性。所提出的方法可能有助于实现更可靠的早期诊断，从而在临床实践中促进及时且有针对性的干预。