本研究旨在通过分析临床单光子发射计算机断层扫描（SPECT）影像数据，利用深度卷积神经网络（DCNN）模型，提高核医学医师的诊断性能，并以两个多中心数据集进行验证。在这个多中心回顾性研究中，收集了3194个SPECT甲状腺图像，用于模型训练（n = 2067）、内部验证（n = 514）和外部验证（n = 613）。首先，使用四个预训练的DCNN模型（AlexNet、ShuffleNetV2、MobileNetV3和ResNet-34）对甲状腺疾病类型（即Graves病、亚急性甲状腺炎、甲状腺肿瘤和正常甲状腺）的多个医学图像进行了测试。最佳表现的模型然后进行了五折交叉验证，进一步评估其性能，并将该模型的诊断性能与初级和高级核医学医师进行了比较。最后，使用梯度加权类激活映射（gradient-weighted class activation mapping）对特定类别的注意力区域进行可视化。 四个预训练神经网络中的每一个在SPECT甲状腺图像分类上都达到了超过0.85的整体准确率。改进后的ResNet-34模型表现最佳，准确率达到0.944。在内部验证集中，与高级核医学医师相比，ResNet-34模型的准确率显著提高（p < 0.001），提高了近10%。我们的模型在外部数据集上实现了总体准确率为0.931，明显高于高级医师的准确率（0.931 vs. 0.868，p < 0.001）。 基于DCNN的模型在诊断甲状腺闪烁图像方面表现良好。与核医学医师相比，DCNN模型在识别Graves病、亚急性甲状腺炎和甲状腺肿瘤方面具有更高的敏感性和更大的特异性，说明深度学习模型在提高辅助临床医生诊断效率方面的可行性。 版权所有©2023 Zhao, Zheng, Zhang, Rao, Li, Fang, Huang, Zhang和Yuan。

The aim of this study was to improve the diagnostic performance of nuclear medicine physicians using a deep convolutional neural network (DCNN) model and validate the results with two multicenter datasets for thyroid disease by analyzing clinical single-photon emission computed tomography (SPECT) image data.In this multicenter retrospective study, 3194 SPECT thyroid images were collected for model training (n=2067), internal validation (n=514) and external validation (n=613). First, four pretrained DCNN models (AlexNet, ShuffleNetV2, MobileNetV3 and ResNet-34) for were tested multiple medical image classification of thyroid disease types (i.e., Graves' disease, subacute thyroiditis, thyroid tumor and normal thyroid). The best performing model was then subjected to fivefold cross-validation to further assess its performance, and the diagnostic performance of this model was compared with that of junior and senior nuclear medicine physicians. Finally, class-specific attentional regions were visualized with attention heatmaps using gradient-weighted class activation mapping.Each of the four pretrained neural networks attained an overall accuracy of more than 0.85 for the classification of SPECT thyroid images. The improved ResNet-34 model performed best, with an accuracy of 0.944. For the internal validation set, the ResNet-34 model showed higher accuracy (p < 0.001) when compared to that of the senior nuclear medicine physician, with an improvement of nearly 10%. Our model achieved an overall accuracy of 0.931 for the external dataset, a significantly higher accuracy than that of the senior physician (0.931 vs. 0.868, p < 0.001).The DCNN-based model performed well in terms of diagnosing thyroid scintillation images. The DCNN model showed higher sensitivity and greater specificity in identifying Graves' disease, subacute thyroiditis, and thyroid tumors compared to those of nuclear medicine physicians, illustrating the feasibility of deep learning models to improve the diagnostic efficiency for assisting clinicians.Copyright © 2023 Zhao, Zheng, Zhang, Rao, Li, Fang, Huang, Zhang and Yuan.