Dan Liu,1,* Ke Yang,2,* Chunquan Zhang,1,* Dandan Xiao,1 Yu Zhao1 

1Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People’s Republic of China; 2The First in-Patient Department, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Dan Liu, Department of Ultrasound, The Second Affiliated Hospital of Nanchang University, No. 1, Minde Road, Donghu District, Nanchang, Jiangxi, 330006, People’s Republic of China, Tel +86-15170076809, Email ultrald@163.com

Background: Interpretation of ultrasound findings of thyroid nodules is subjective and labor-intensive for radiologists. Artificial intelligence (AI) is a relatively objective and efficient technology. We aimed to establish a fully automatic detection and diagnosis system for thyroid nodules based on AI technology by analyzing ultrasound video sequences.
Patients and Methods: We prospectively acquired dynamic ultrasound videos of 1067 thyroid nodules (804 for training and 263 for validation) from December 2018 to January 2021. All the patients underwent hemithyroidectomy or total thyroidectomy. Dynamic ultrasound videos were used to develop an AI system consisting of two deep learning models that could automatically detect and diagnose thyroid nodules. Average precision (AP) was used to estimate the performance of the detection model. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of the diagnostic model.
Results: Location and shape were accurately detected with a high AP of 0.914 in the validation cohort. The AUC of the diagnostic model was 0.953 in the validation cohort. The sensitivity and specificity of junior and senior radiologists were 76.9% vs 78.3% and 68.4% vs 81.1%, respectively. The diagnostic performance of the AI diagnostic model was superior to that of junior radiologists (P = 0.016) and was not significantly different from that of senior radiologists (P = 0.281).
Conclusion: We established a fully automatic detection and diagnosis system for thyroid nodules based on ultrasound video using an AI approach that can be conveniently applied to optimize the management of patients with thyroid nodules.

Keywords: Thyroid nodule, Ultrasonography, Artificial intelligence, Deep learning, Radiomics