Unknown

Dataset Information

0

Diagnosis of Thyroid Nodules: Performance of a Deep Learning Convolutional Neural Network Model vs. Radiologists.


ABSTRACT: Computer-aided diagnosis (CAD) systems hold potential to improve the diagnostic accuracy of thyroid ultrasound (US). We aimed to develop a deep learning-based US CAD system (dCAD) for the diagnosis of thyroid nodules and compare its performance with those of a support vector machine (SVM)-based US CAD system (sCAD) and radiologists. dCAD was developed by using US images of 4919 thyroid nodules from three institutions. Its diagnostic performance was prospectively evaluated between June 2016 and February 2017 in 286 nodules, and was compared with those of sCAD and radiologists, using logistic regression with the generalized estimating equation. Subgroup analyses were performed according to experience level and separately for small thyroid nodules 1-2?cm. There was no difference in overall sensitivity, specificity, positive predictive value (PPV), negative predictive value and accuracy (all p?>?0.05) between radiologists and dCAD. Radiologists and dCAD showed higher specificity, PPV, and accuracy than sCAD (all p?

SUBMITTER: Park VY 

PROVIDER: S-EPMC6882804 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

altmetric image

Publications

Diagnosis of Thyroid Nodules: Performance of a Deep Learning Convolutional Neural Network Model vs. Radiologists.

Park Vivian Y VY   Han Kyunghwa K   Seong Yeong Kyeong YK   Park Moon Ho MH   Kim Eun-Kyung EK   Moon Hee Jung HJ   Yoon Jung Hyun JH   Kwak Jin Young JY  

Scientific reports 20191128 1


Computer-aided diagnosis (CAD) systems hold potential to improve the diagnostic accuracy of thyroid ultrasound (US). We aimed to develop a deep learning-based US CAD system (dCAD) for the diagnosis of thyroid nodules and compare its performance with those of a support vector machine (SVM)-based US CAD system (sCAD) and radiologists. dCAD was developed by using US images of 4919 thyroid nodules from three institutions. Its diagnostic performance was prospectively evaluated between June 2016 and F  ...[more]

Similar Datasets

| S-EPMC7498581 | biostudies-literature
| S-EPMC7199615 | biostudies-literature
| S-EPMC6485748 | biostudies-literature
| S-EPMC8698578 | biostudies-literature
| S-EPMC6360226 | biostudies-literature
| S-EPMC8501016 | biostudies-literature
| S-EPMC10034408 | biostudies-literature
| S-EPMC4992049 | biostudies-other
| S-EPMC7243989 | biostudies-literature
| S-EPMC5537102 | biostudies-other