Diagnosis of thyroid nodules on ultrasonography by a deep convolutional neural network
| العنوان: | Diagnosis of thyroid nodules on ultrasonography by a deep convolutional neural network |
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| المؤلفون: | Young Mi Park, Yu-Mee Sohn, Eun Kyung Kim, Mirinae Seo, Kyunghwa Han, Jin Hwa Lee, Eun Ju Son, Jung Hee Shin, Sung-Won Kim, Jung Hyun Yoon, Jin Young Kwak, Jieun Koh, Eunjung Lee, Mi ri Kwon |
| المصدر: | Scientific Reports, Vol 10, Iss 1, Pp 1-9 (2020) Scientific Reports |
| بيانات النشر: | Springer Science and Business Media LLC, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Adult, Male, Thyroid nodules, medicine.medical_specialty, education, lcsh:Medicine, Convolutional neural network, Article, 030218 nuclear medicine & medical imaging, Cohort Studies, Diagnosis, Differential, 03 medical and health sciences, Endocrinology, Deep Learning, 0302 clinical medicine, Radiologists, Republic of Korea, Humans, Medicine, Thyroid Nodule, lcsh:Science, Expert Testimony, Cancer, Retrospective Studies, Ultrasonography, Multidisciplinary, Training set, business.industry, Deep learning, lcsh:R, Middle Aged, University hospital, medicine.disease, Computational biology and bioinformatics, Multicenter study, Area Under Curve, 030220 oncology & carcinogenesis, Test set, Female, lcsh:Q, Neural Networks, Computer, Radiology, Artificial intelligence, business, Algorithms |
| الوصف: | The purpose of this study was to evaluate and compare the diagnostic performances of the deep convolutional neural network (CNN) and expert radiologists for differentiating thyroid nodules on ultrasonography (US), and to validate the results in multicenter data sets. This multicenter retrospective study collected 15,375 US images of thyroid nodules for algorithm development (n = 13,560, Severance Hospital, SH training set), the internal test (n = 634, SH test set), and the external test (n = 781, Samsung Medical Center, SMC set; n = 200, CHA Bundang Medical Center, CBMC set; n = 200, Kyung Hee University Hospital, KUH set). Two individual CNNs and two classification ensembles (CNNE1 and CNNE2) were tested to differentiate malignant and benign thyroid nodules. CNNs demonstrated high area under the curves (AUCs) to diagnose malignant thyroid nodules (0.898–0.937 for the internal test set and 0.821–0.885 for the external test sets). AUC was significantly higher for CNNE2 than radiologists in the SH test set (0.932 vs. 0.840, P P = 0.113, 0.126, and 0.690). CNN showed diagnostic performances comparable to expert radiologists for differentiating thyroid nodules on US in both the internal and external test sets. |
| تدمد: | 2045-2322 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::749964847cff009f2fe840b3c34f4a28 https://doi.org/10.1038/s41598-020-72270-6 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....749964847cff009f2fe840b3c34f4a28 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20452322 |
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