Objectives: The purpose is to apply a previously validated deep learning algorithm to a new thyroid nodule ultrasound image dataset and compare its performances with radiologists. Methods: Prior study presented an algorithm which is able to detect thyroid nodules and then make malignancy classifications with two ultrasound images. A multi-task deep convolutional neural network was trained from 1278 nodules and originally tested with 99 separate nodules. The results were comparable with that of radiologists. The algorithm was further tested with 378 nodules imaged with ultrasound machines from different manufacturers and product types than the training cases. Four experienced radiologists were requested to evaluate the nodules for comparison with deep learning. Results: The Area Under Curve (AUC) of the deep learning algorithm and four radiologists were calculated with parametric, binormal estimation. For the deep learning algorithm, the AUC was 0.70 (95% CI: 0.64 - 0.75). The AUC of radiologists were 0.66 (95% CI: 0.61 - 0.71), 0.67 (95% CI:0.62 - 0.73), 0.68 (95% CI: 0.63 - 0.73), and 0.66 (95%CI: 0.61 - 0.71). Conclusion: In the new testing dataset, the deep learning algorithm achieved similar performances with all four radiologists.

翻译：目标:目的是对一个新的甲状腺结核超声波图像数据集应用一个先前经过验证的深层学习算法,并将其性能与放射学家进行比较。方法:先前的研究提出一种算法,能够检测甲状腺结核,然后用两个超声波图像进行恶性分类。从1278个结核中培训了一个多任务深演动神经网络,最初用99个单独的结核进行了测试。结果与放射学家相仿。该算法还用378台超声波机进一步测试了378台结核图象,该算法来自不同的制造商和产品类型,而不是培训案例。请4名经验丰富的放射学家对结核进行评估,以便与深层学习进行比较。结果:深层学习算法(AUC)下区域(AUC)和4个放射学家用参数估算计算。对于深层算法来说,ACU是0.70(95%的CI:0.64-0.75)。AUC是0.66(95%的CI:0.763(95%的CI):0.683(95%的CI:95%的CI:0.73-0.73)。