为了探索深度神经网络（DNN）在甲状腺肿瘤病理图像中的诊断价值和临床应用潜力，回顾性分析了559名甲状腺肿瘤患者共计799张病理甲状腺图像。病理类型包括乳头状甲状腺癌（PTC）、髓样甲状腺癌（MTC）、滤泡状甲状腺癌（FTC）、腺性肿大、腺瘤和正常甲状腺。将数据集划分为训练集和测试集，在训练集数据上分别训练了Resnet50、Resnext50、EfficientNet和Densenet121，并在测试集数据上进行测试，以确定不同病理类型的诊断效率，并进一步分析误诊原因。 四个模型的召回率、精确率、负预测值（NPV）、准确率、特异度和F1分数范围为33.33%至100.00%。曲线下面积（AUC）范围为0.822至0.994，Kappa系数范围为0.7508至0.7713。然而，对FTC、腺瘤和腺性肿大的诊断性能略低于其他类型的病理组织。 通过学习甲状腺病理图像，DNN模型在分类甲状腺肿瘤的任务中取得了令人满意的结果。这些结果表明了DNN模型在甲状腺肿瘤组织病理学的高效诊断方面的潜力。 © 2023. BioMed Central Ltd., part of Springer Nature.

To explore the distinguishing diagnostic value and clinical application potential of deep neural networks (DNN) for pathological images of thyroid tumors.A total of 799 pathological thyroid images of 559 patients with thyroid tumors were retrospectively analyzed. The pathological types included papillary thyroid carcinoma (PTC), medullary thyroid carcinoma (MTC), follicular thyroid carcinoma (FTC), adenomatous goiter, adenoma, and normal thyroid gland. The dataset was divided into a training set and a test set. Resnet50, Resnext50, EfficientNet, and Densenet121 were trained using the training set data and tested with the test set data to determine the diagnostic efficiency of different pathology types and to further analyze the causes of misdiagnosis.The recall, precision, negative predictive value (NPV), accuracy, specificity, and F1 scores of the four models ranged from 33.33% to 100.00%. The area under curve (AUC) ranged from 0.822 to 0.994, and the Kappa coefficient ranged from 0.7508 to 0.7713. However, the performance of diagnosing FTC, adenoma, and adenomatous goiter was slightly inferior to other types of pathological tissues.The DNN model achieved satisfactory results in the task of classifying thyroid tumors by learning thyroid pathology images. These results indicate the potential of the DNN model for the efficient diagnosis of thyroid tumor histopathology.© 2023. BioMed Central Ltd., part of Springer Nature.