Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound

Su Yeon Ko, Ji Hye Lee, Jung Hyun Yoon, Hyesun Na, Eunhye Hong, Kyunghwa Han, Inkyung Jung, Eun Kyung Kim, Hee Jung Moon, Vivian Y. Park, Eunjung Lee, Jin Young Kwak

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)885-891
Number of pages7
JournalHead and Neck
Volume41
Issue number4
DOIs
Publication statusPublished - 2019 Apr

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Thyroid Nodule
Thyroid Gland
Area Under Curve
Neoplasms
Ultrasonography
Radiologists

All Science Journal Classification (ASJC) codes

  • Otorhinolaryngology

Cite this

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. / Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound. In: Head and Neck. 2019 ; Vol. 41, No. 4. pp. 885-891.
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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.",
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Ko, SY, Lee, JH, Yoon, JH, Na, H, Hong, E, Han, K, Jung, I, Kim, EK, Moon, HJ, Park, VY, Lee, E & Kwak, JY 2019, 'Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound', Head and Neck, vol. 41, no. 4, pp. 885-891. https://doi.org/10.1002/hed.25415

Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound. / 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.

In: Head and Neck, Vol. 41, No. 4, 04.2019, p. 885-891.

Research output: Contribution to journalArticle

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AU - Lee, Ji Hye

AU - Yoon, Jung Hyun

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AU - Hong, Eunhye

AU - Han, Kyunghwa

AU - Jung, Inkyung

AU - Kim, Eun Kyung

AU - Moon, Hee Jung

AU - Park, Vivian Y.

AU - Lee, Eunjung

AU - Kwak, Jin Young

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N2 - 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.

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