Haugen, B. R. et al. 2015 American thyroid association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: The American thyroid association guidelines task force on thyroid nodules and differentiated thyroid cancer. Thyroid26(1), 1–133 (2016).
Vaccarella, S. et al. Worldwide thyroid-cancer epidemic? The increasing impact of overdiagnosis. N. Engl. J. Med.375(7), 614–617 (2016).
Hoang, J. K. et al. Interobserver variability of sonographic features used in the American college of radiology thyroid imaging reporting and data system. Am. J. Roentgenol.211(1), 162–167 (2018).
Hong, Y. et al. Conventional US, elastography, and contrast enhanced US features of papillary thyroid microcarcinoma predict central compartment lymph node metastases. Sci. Rep.5, 7748 (2015).
Gharib, H. et al. American Association of Clinical Endocrinologists, American College of Endocrinology, and Associazione Medici Endocrinologi Medical Guidelines for Clinical Practice for the diagnosis and management of thyroid nodules—2016 update. Endocr. Pract.22, 622–639 (2016).
Guth, S., Theune, U., Aberle, J., Galach, A. & Bamberger, C. M. Very high prevalence of thyroid nodules detected by high frequency (13MHz) ultrasound examination. Eur. J. Clin. Investig.39, 699–706 (2009).
Dayan, C. M., Okosieme, O. E. & Taylor, P. Thyroid dysfunction. In Clinical Biochemistry: Metabolic and Clinical Aspects 3rd edn (eds Marshall, W. J. et al.) (Elsevier, Amsterdam, 2014).
Nygaard, B., Jensen, E. W., Kvetny, J., Jarlov, A. & Faber, J. Effect of combination therapy with thyroxine (T4) and 3,5,3’-triiodothyronine versus T4 monotherapy in patients with hypothyroidism, a double-blind, randomised cross-over study. Eur. J. Endocrinol.161(6), 895–902 (2019).
Tessler, F. N. et al. ACR thyroid imaging, reporting and data system (TI-RADS): White paper of the ACR TI-RADS committee. J. Am. Coll. Radiol.14(5), 587–595 (2017).
Hoang, J. K. et al. Reduction in thyroid nodule biopsies and improved accuracy with American college of radiology thyroid imaging reporting and data system. Radiology287(1), 185–193 (2018).
Griffin, A. S. et al. Improved quality of thyroid ultrasound reports after implementation of the ACR thyroid imaging reporting and data system nodule lexicon and risk stratification system. J. Am. Coll. Radiol.15(5), 743–748 (2018).
Lecun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature521(7553), 436–444 (2015).
Schmidhuber, J. Deep learning in neural networks: An overview. Neural Netw.61, 85–117 (2015).
Ramachandran, R., Rajeev, D. C., Krishnan, S. G. & Subathra, P. Deep learning an overview. IJAER10(10), 25433–25448 (2015).
Wang, P., et al. Large-scale continuous gesture recognition using convolutional neural networks. IEEE Inter. Conf. on Pattern Recognition (2016).
Ren, S., He, K., Girshick, R. & Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. Adv. Neural. Inf. Process. Syst. 91–99 (2015).
Memisevic, R. & Hinton, G. E. Learning to represent spatial transformations with factored higher-order Boltzmann machines. Neural Comput.22(6), 1473 (2010).
Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature542(7639), 115–118 (2017).
Gulshan, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA316(22), 2402–2410 (2016).
Erickson, B. J., Korfiatis, P., Akkus, Z. & Kline, T. L. Machine learning for medical imaging. Radiographics37(2), 505–515 (2017).
Mazurowski, M. A., Buda, M., Saha, A. & Bashir, M. R. Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI. J. Magn. Reson. Imaging49(4), 939–954 (2019).
Lee, H. et al. Fully automated deep learning system for bone age assessment. J. Digit. Imaging30(4), 427–441 (2017).
Chi, J. et al. Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network. J. Digit. Imaging30(4), 477–486 (2017).
Ma, J., Wu, F., Zhu, J., Xu, D. & Kong, D. A pre-trained convolutional neural network based method for thyroid nodule diagnosis. Ultrasonics73, 221–230 (2017).
Dargan, S., Kumar, M., Ayyagari, M. R. & Kumar, G. A survey of deep learning and its applications: A new paradigm to machine learning. Arch. Comput. Methods Eng.
Shin, H. C. et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging.35(5), 1285–1298 (2016).
Anwar, S. M. et al. Medical image analysis using convolutional neural networks: A review. J. Med. Syst.42(11), 226 (2018).
Moon, W. J. et al. Benign and malignant thyroid nodules: US differentiation—Multicenter retrospective study. Radiology247, 762–770 (2008).
Choi, S. H., Kim, E., Kwak, J. Y., Kim, M. J. & Son, E. J. Interobserver and intraobserver variations in ultrasound assessment of thyroid nodules. Thyroid20, 167–172 (2010).
Park, C. S. et al. Observer variability in the sonographic evaluation of thyroid nodules. J. Clin. Ultrasound38, 287–293 (2010).
Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. Proc. Int. Conf. Learn. Representations (2015).
DeLong, E. R., DeLong, D. M. & Clarke-Pearson, D. L. Comparing areas under two or more correlated receiver operating characteristics curves: A nonparamentric approach. Biometrics44(3), 837–845 (1988).
Sun, X. & Xu, W. Fast implementation of DeLong’s algorithm for comparing the areas under correlated receiver operating characteristic curves. IEEE Signal. Proc. Lett.21(11), 1389–1393 (2014).
Xia, J. et al. Ultrasound-based differentiation of malignant and benign thyroid Nodules: An extreme learning machine approach. Comput. Methods Progr. Biomed.147, 37–49 (2017).
Pereira, C., Dighe, M., Alessio A. M. Comparison of machine learned approaches for thyroid nodule characterization from shear wave elastography images. Proc. SPIE Med. Imaging Comput. Aided Diagn. 105751X (2018).
Buda, M. et al. Management of thyroid nodules seen on US images: Deep learning may match performance of radiologist. Radiology292(3), 695–701 (2019).