Deep learning to diagnose Hashimoto’s thyroiditis from sonographic images
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Published:2022-06-29
Issue:1
Volume:13
Page:
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ISSN:2041-1723
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Container-title:Nature Communications
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language:en
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Short-container-title:Nat Commun
Author:
Zhang Qiang, Zhang Sheng, Pan Yi, Sun Lin, Li Jianxin, Qiao Yu, Zhao Jing, Wang Xiaoqing, Feng Yixing, Zhao Yanhui, Zheng Zhiming, Yang Xiangming, Liu Lixia, Qin Chunxin, Zhao Ke, Liu Xiaonan, Li Caixia, Zhang Liuyang, Yang Chunrui, Zhuo Na, Zhang Hong, Liu Jie, Gao Jinglei, Di Xiaoling, Meng Fanbo, Zhang Linlei, Wang Yuxuan, Duan Yuansheng, Shen Hongru, Li YangORCID, Yang Meng, Yang Yichen, Xin Xiaojie, Wei Xi, Zhou Xuan, Jin Rui, Zhang Lun, Wang Xudong, Song Fengju, Zheng Xiangqian, Gao Ming, Chen KexinORCID, Li XiangchunORCID
Abstract
AbstractHashimoto’s thyroiditis (HT) is the main cause of hypothyroidism. We develop a deep learning model called HTNet for diagnosis of HT by training on 106,513 thyroid ultrasound images from 17,934 patients and test its performance on 5051 patients from 2 datasets of static images and 1 dataset of video data. HTNet achieves an area under the receiver operating curve (AUC) of 0.905 (95% CI: 0.894 to 0.915), 0.888 (0.836–0.939) and 0.895 (0.862–0.927). HTNet exceeds radiologists’ performance on accuracy (83.2% versus 79.8%; binomial test, p < 0.001) and sensitivity (82.6% versus 68.1%; p < 0.001). By integrating serologic markers with imaging data, the performance of HTNet was significantly and marginally improved on the video (AUC, 0.949 versus 0.888; DeLong’s test, p = 0.004) and static-image (AUC, 0.914 versus 0.901; p = 0.08) testing sets, respectively. HTNet may be helpful as a tool for the management of HT.
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary
Reference25 articles.
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