Ultrasound image analysis using deep learning algorithm for the diagnosis of thyroid nodules.

Abstract:

:Fine needle aspiration (FNA) is the procedure of choice for evaluating thyroid nodules. It is indicated for nodules >2 cm, even in cases of very low suspicion of malignancy. FNA has associated risks and expenses. In this study, we developed an image analysis model using a deep learning algorithm and evaluated if the algorithm could predict thyroid nodules with benign FNA results.Ultrasonographic images of thyroid nodules with cytologic or histologic results were retrospectively collected. For algorithm training, 1358 (670 benign, 688 malignant) thyroid nodule images were input into the Inception-V3 network model. The model was pretrained to classify nodules as benign or malignant using the ImageNet database. The diagnostic performance of the algorithm was tested with the prospectively collected internal (n = 55) and external test sets (n = 100).For the internal test set, 20 of the 21 FNA malignant nodules were correctly classified as malignant by the algorithm (sensitivity, 95.2%); and of the 22 nodules algorithm classified as benign, 21 were FNA benign (negative predictive value [NPV], 95.5%). For the external test set, 47 of the 50 FNA malignant nodules were correctly classified by the algorithm (sensitivity, 94.0%); and of the 31 nodules the algorithm classified as benign, 28 were FNA benign (NPV, 90.3%).The sensitivity and NPV of the deep learning algorithm shown in this study are promising. Artificial intelligence may assist clinicians to recognize nodules that are likely to be benign and avoid unnecessary FNA.

journal_name

Medicine (Baltimore)

journal_title

Medicine

authors

Song J,Chai YJ,Masuoka H,Park SW,Kim SJ,Choi JY,Kong HJ,Lee KE,Lee J,Kwak N,Yi KH,Miyauchi A

doi

10.1097/MD.0000000000015133

subject

Has Abstract

pub_date

2019-04-01 00:00:00

pages

e15133

issue

15

eissn

0025-7974

issn

1536-5964

pii

00005792-201904120-00041

journal_volume

98

pub_type

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