Deep learning to diagnose Hashimoto’s thyroiditis from sonographic images

Hashimoto’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...

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Detalles Bibliográficos
Autores principales: 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, Yang, Yang, Meng, Yang, Yichen, Xin, Xiaojie, Wei, Xi, Zhou, Xuan, Jin, Rui, Zhang, Lun, Wang, Xudong, Song, Fengju, Zheng, Xiangqian, Gao, Ming, Chen, Kexin, Li, Xiangchun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9243092/
https://www.ncbi.nlm.nih.gov/pubmed/35768466
http://dx.doi.org/10.1038/s41467-022-31449-3
Descripción
Sumario:Hashimoto’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.

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