{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:50:48Z","timestamp":1788915048884,"version":"build-2803163510"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T00:00:00Z","timestamp":1615852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014175","name":"Shanghai Municipal Health and Family Planning Commission","doi-asserted-by":"publisher","award":["2018ZHYL0106"],"award-info":[{"award-number":["2018ZHYL0106"]}],"id":[{"id":"10.13039\/501100014175","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81671688, 82071923"],"award-info":[{"award-number":["81671688, 82071923"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>The aim of this study is to develop a model using Deep Neural Network (DNN) to diagnose thyroid nodules in patients with Hashimoto\u2019s Thyroiditis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this retrospective study, we included 2,932 patients with thyroid nodules who underwent thyroid ultrasonogram in our hospital from January 2017 to August 2019. 80% of them were included as training set and 20% as test set. Nodules suspected for malignancy underwent FNA or surgery for pathological results. Two DNN models were trained to diagnose thyroid nodules, and we chose the one with better performance. The features of nodules as well as parenchyma around nodules will be learned by the model to achieve better performance under diffused parenchyma. 10-fold cross-validation and an independent test set were used to evaluate the performance of the algorithm. The performance of the model was compared with that of the three groups of radiologists with clinical experience of &amp;lt;5 years, 5\u201310 years, &amp;gt;10 years respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In total, 9,127 images were collected from 2,932 patients with 7,301 images for the training set and 1,806 for the test set. 56% of the patients enrolled had Hashimoto\u2019s Thyroiditis. The model achieved an AUC of 0.924 for distinguishing malignant and benign nodules in the test set. It showed similar performance under diffused thyroid parenchyma and normal parenchyma with sensitivity of 0.881 <jats:italic>versus<\/jats:italic> 0.871 (p = 0.938) and specificity of 0.846 <jats:italic>versus<\/jats:italic> 0.822 (p = 0.178). In patients with HT, the model achieved an AUC of 0.924 to differentiate malignant and benign nodules which was significantly higher than that of the three groups of radiologists (AUC = 0.824, 0.857, 0.863 respectively, p &amp;lt; 0.05).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The model showed high performance in diagnosing thyroid nodules under both normal and diffused parenchyma. In patients with Hashimoto\u2019s Thyroiditis, the model showed a better performance compared to radiologists with various years of experience.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fonc.2021.614172","type":"journal-article","created":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T06:41:11Z","timestamp":1615876871000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Using Deep Neural Network to Diagnose Thyroid Nodules on Ultrasound in Patients With Hashimoto\u2019s 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