Deep learning-based artificial intelligence model to assist thyroid nodule diagnosis and management: a multicentre diagnostic study

作者：Sui Peng, Yihao Liu, Weiming Lv, Longzhong Liu, Qian Zhou, Hong Yang, Jie Ren, Guangjian Liu, Xiaodong Wang, Xuehua Zhang, Qiang Du, Fangxing Nie, Gao Huang, Yuchen Guo, Jie Li, Jinyu Liang, Hangtong Hu, Han Xiao, Han Xiao, Ze-Long Liu, Fenghua Lai, Qiuyi Zheng, Haibo Wang, Yanbing Li, Erik K. Alexander, Wei Wang, Haipeng Xiao, Haipeng Xiao · 发表于：The Lancet Digital Health · 年份：2021 · DOI：10.1016/s2589-7500(21)00041-8 · 被引用次数：388 · 研究领域：Thyroid Cancer Diagnosis and Treatment、AI in cancer detection、Artificial Intelligence in Healthcare and Education

BACKGROUND: Strategies for integrating artificial intelligence (AI) into thyroid nodule management require additional development and testing. We developed a deep-learning AI model (ThyNet) to differentiate between malignant tumours and benign thyroid nodules and aimed to investigate how ThyNet could help radiologists improve diagnostic performance and avoid unnecessary fine needle aspiration. METHODS: ThyNet was developed and trained on 18 049 images of 8339 patients (training set) from two hospitals (the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China, and Sun Yat-sen University Cancer Center, Guangzhou, China) and tested on 4305 images of 2775 patients (total test set) from seven hospitals (the First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China; the Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; the Guangzhou Army General Hospital, Guangzhou, China; the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; the First Affiliated Hospital of Sun Yat-sen University; Sun Yat-sen University Cancer Center; and the First Affiliated Hospital of Guangxi Medical University, Nanning, China) in three stages. All nodules in the training and total test set were pathologically confirmed. The diagnostic performance of ThyNet was first compared with 12 radiologists (test set A); a ThyNet-assisted strategy, in which ThyNet assisted diagnoses made by radiologists, was developed to improve diagn...