Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound. Issue 4 (4th February 2019)

• Record Type: Journal Article
• Authors: Ko, Su Yeon; Lee, Ji Hye; Yoon, Jung Hyun; Na, Hyesun; Hong, Eunhye; Han, Kyunghwa; Jung, Inkyung; Kim, Eun‐Kyung; Moon, Hee Jung; Park, Vivian Y.; Lee, Eunjung; Kwak, Jin Young
• Is Part Of: Head & neck. Volume 41:Issue 4(2019)
• Page Start: 885
• Page End: 891
• Publication Date: 2019-02-04
• Subjects: convolutional neural network (CNN) -- deep learning -- thyroid cancer -- thyroid nodule -- ultrasound; Head -- Diseases -- Periodicals; Neck -- Diseases -- Periodicals; Head -- Periodicals; Neck -- Periodicals; Face -- Periodicals; 617.51059
• DOI: 10.1002/hed.25415
• Languages: English
• ISSNs: 1043-3074

Abstract

Abstract: Background: We designed a deep convolutional neural network (CNN) to diagnose thyroid malignancy on ultrasound (US) and compared the diagnostic performance of CNN with that of experienced radiologists. Methods: Between May 2012 and February 2015, 589 thyroid nodules in 519 patients were diagnosed as benign or malignant by surgical excision. Experienced radiologists retrospectively reviewed the US of the thyroid nodules in a test set. CNNs were trained and tested using retrospective data of 439 and 150 US images, respectively. Diagnostic performances were compared between the two groups. Results: Of the 589 thyroid nodules, 396 were malignant and 193 were benign. The area under the curve (AUC) for diagnosing thyroid malignancy was 0.805‐0.860 for radiologists. The AUCs for diagnosing thyroid malignancy for the three CNNs were 0.845, 0.835, and 0.850. There was no significant difference in AUC between radiologists and CNNs. Conclusions: CNNs showed comparable diagnostic performance compared to experienced radiologists in differentiating thyroid malignancy on US.