Adaptive Dual-Task Deep Learning for Automated Thyroid Cancer Triaging at Screening US

“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop an adaptive dual-task deep learning model (ThyNet-S) for detection and classification of thyroid lesions at US screening. Materials and Methods The retrospective study used a multicenter dataset comprising 35008 thyroid US images of 23294 individual examinations (mean age, 40.4 years ± 13.1[SD], 17587 female) from 7 medical centers during January 2009 and December 2021. Of these, 29004 images were used for model development and 6004 images for validation. The model determined cancer risk for each image and automatically triaged images with normal thyroid and benign nodules by dynamically integrating lesion detection through pixel-level feature analysis and lesion classification through deep semantic features analysis. Diagnostic performance of screening assisted by the model (ThyNet-S triaged screening) and traditional screening (radiologists alone) was assessed by comparing sensitivity, specificity, accuracy and AUC using McNemar’s test and Delong test. The influence of ThyNet-S on radiologist workload and clinical decision-making was also assessed. Results ThyNet-S-assisted triaged screening achieved higher AUC than original screening in six senior and six junior radiologists (0.93 versus 0.91, and 0.92 versus 0.88, respectively, all P < .001). The model improved sensitivity for junior radiologists (88.2% versus 86.8%, P <.001). Notably, the model reduced radiologists’ workload by triaging 60.4% of cases as not potentially malignant, which did not require further interpretation. The model simultaneously decreased unnecessary fine needle aspiration rate from 38.7% to 14.9% and 11.5% when used independently or in combination with Thyroid Imaging Reporting and Data System, respectively. Conclusion ThyNet-S improved efficiency of thyroid cancer screening and optimized clinical decision-making. ©RSNA, 2025