Application of Automatic Thyroid Nodule Recognition Based on Deep Learning in Ultrasonic Image
WANG Hongjiea, YU Xiab, GAO Qiangc
a. Department of Medical Equipment; b. Department of Ultrasound; c. Department of Radiology, Weihai Maternal and Child Health Hospital, Weihai Shandong 26400, China
Abstract：Objective To construct and validate a deep learning model for automatic recognition of thyroid nodules in order to improve the level of recognition and diagnosis of thyroid nodules. Methods A total of 6321 thyroid images from January 2013 to January 2018 were selected, of which 2000 images diagnosed as multiple nodules and 1200 images diagnosed as single nodules were used for deep learning model training, the other 3121 images were used for deep learning model validation and were submitted to 4 clinicians for diagnosis. Finally, statistical analysis was carried out. Results Deep learning method surpassed ultrasound physicians in positive expectation rate, negative expectation rate, diagnostic sensitivity, diagnostic efficiency and diagnostic specificity. The positive expectation rate of deep learning method was 10.00% points higher than that of senior ultrasound doctors, the negative expectation rate was 5.02% points higher, and the diagnostic efficiency was 10.24% points higher. Conclusion The deep learning model constructed in this study has high accuracy in the diagnosis of thyroid nodules, and can assist physicians in real-time diagnosis of thyroid nodules in ultrasound diagnosis. It is feasible to apply deep learning method to the clinical diagnosis of thyroid nodules in ultrasound images.
王洪杰a，于霞b，高强c. 基于深度学习的甲状腺结节自动识别方法在超声图像中的应用[J]. 中国医疗设备, 2019, 34(10): 72-74.
WANG Hongjiea, YU Xiab, GAO Qiangc. Application of Automatic Thyroid Nodule Recognition Based on Deep Learning in Ultrasonic Image. China Medical Devices, 2019, 34(10): 72-74.
