Ni Yu-Ching, Tseng Fan-Pin, Pai Ming-Chyi, Hsiao Ing-Tsung, Lin Kun-Ju, Lin Zhi-Kun, Lin Chia-Yu, Chiu Pai-Yi, Hung Guang-Uei, Chang Chiung-Chih, Chang Ya-Ting, Chuang Keh-Shih
Health Physics Division, Institute of Nuclear Energy Research, Atomic Energy Council, Taoyuan 325, Taiwan.
Department of Biomedical Engineering and Environmental Sciences, National Tsing-Hua University, Hsinchu 300, Taiwan.
Diagnostics (Basel). 2021 Nov 12;11(11):2091. doi: 10.3390/diagnostics11112091.
The correct differential diagnosis of dementia has an important impact on patient treatment and follow-up care strategies. Tc-99m-ECD SPECT imaging, which is low cost and accessible in general clinics, is used to identify the two common types of dementia, Alzheimer's disease (AD) and Lewy body dementia (LBD). Two-stage transfer learning technology and reducing model complexity based on the ResNet-50 model were performed using the ImageNet data set and ADNI database. To improve training accuracy, the three-dimensional image was reorganized into three sets of two-dimensional images for data augmentation and ensemble learning, then the performance of various deep learning models for Tc-99m-ECD SPECT images to distinguish AD/normal cognition (NC), LBD/NC, and AD/LBD were investigated. In the AD/NC, LBD/NC, and AD/LBD tasks, the AUC values were around 0.94, 0.95, and 0.74, regardless of training models, with an accuracy of 90%, 87%, and 71%, and F1 scores of 89%, 86%, and 76% in the best cases. The use of transfer learning and a modified model resulted in better prediction results, increasing the accuracy by 32% for AD/NC. The proposed method is practical and could rapidly utilize a deep learning model to automatically extract image features based on a small number of SPECT brain perfusion images in general clinics to objectively distinguish AD and LBD.
痴呆的正确鉴别诊断对患者治疗及后续护理策略具有重要影响。锝-99m-乙撑半胱氨酸二聚体（Tc-99m-ECD）单光子发射计算机断层扫描（SPECT）成像成本低且在普通诊所即可进行，用于识别两种常见的痴呆类型，即阿尔茨海默病（AD）和路易体痴呆（LBD）。使用ImageNet数据集和阿尔茨海默病神经成像计划（ADNI）数据库，基于ResNet-50模型进行了两阶段迁移学习技术并降低模型复杂度。为提高训练准确性，将三维图像重新组织为三组二维图像以进行数据增强和集成学习，然后研究了各种深度学习模型对Tc-99m-ECD SPECT图像区分AD/正常认知（NC）、LBD/NC以及AD/LBD的性能。在AD/NC、LBD/NC和AD/LBD任务中，无论训练模型如何，曲线下面积（AUC）值均在0.94、0.95和0.74左右，最佳情况下准确率分别为90%、87%和71%，F1分数分别为89%、86%和76%。迁移学习和改进模型的使用产生了更好的预测结果，AD/NC的准确率提高了32%。所提出的方法具有实用性，能够基于普通诊所中少量的SPECT脑灌注图像快速利用深度学习模型自动提取图像特征，以客观地区分AD和LBD。
