The value of S-Detect in improving the diagnostic performance of radiologists for the differential diagnosis of thyroid nodules.
Aims: To compare the diagnostic value of S-Detect (a computer aided diagnosis system using deep learning) in differentiating thyroid nodules in radiologists with different experience and to assess if S-Detect can improve the diagnostic performance of radiologists.Materials and Methods: Between Febru...
| Publicado en: | Medical Ultrasonography Vol. 22; no. 4; pp. 415 - 424 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Romanian Society of Ultrasonography in Medicine & Biology
2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=147083053&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 147083053 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 18444172 AX1S jtl: Medical Ultrasonography issn: 18444172 maglogo: N pubinfo: dt: 2020 vid: 22 iid: 4 pid: 57537 pub: Romanian Society of Ultrasonography in Medicine & Biology artinfo: ui: 147083053 147083053 NLM32905560 10.11152/mu-2501 NLM32905560 147083053 ppf: 415 ppct: 9 formats: fmt: @attributes: type: P tig: atl: The value of S-Detect in improving the diagnostic performance of radiologists for the differential diagnosis of thyroid nodules. aug: au: Qi Wei Shu-E Zeng Li-Ping Wang Yu-Jing Yan Ting Wang Jian-Wei Xu Meng-Yi Zhang Wen-Zhi Lv Xin-Wu Cui Dietrich, Christoph F. Wei, Qi Zeng, Shu-E Wang, Li-Ping Yan, Yu-Jing Wang, Ting Xu, Jian-Wei Zhang, Meng-Yi Lv, Wen-Zhi Cui, Xin-Wu affil: Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China sug: subj: Thyroid Nodule Thyroid Neoplasms Sensitivity and Specificity Diagnosis, Differential Arthritis Impact Measurement Scales Psychological Tests ab: Aims: To compare the diagnostic value of S-Detect (a computer aided diagnosis system using deep learning) in differentiating thyroid nodules in radiologists with different experience and to assess if S-Detect can improve the diagnostic performance of radiologists.Materials and Methods: Between February 2018 and October 2019, 204 thyroid nodules in 181 patients were included. An experienced radiologist performed ultrasound for thyroid nodules and obtained the result of S-Detect. Four radiologists with different experience on thyroid ultrasound (Radiologist 1, 2, 3, 4 with 1, 4, 9, 20 years, respectively) analyzed the conventional ultrasound images of each thyroid nodule and made a diagnosis of "benign" or "malignant" based on the TI-RADS category. After referring to S-Detect results, they re-evaluated the diagnoses. The diagnostic performance of radiologists was analyzed before and after referring to the results of S-Detect.Results: The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of S-Detect were 77.0, 91.3, 65.2, 68.3 and 90.1%, respectively. In comparison with the less experienced radiologists (radiologist 1 and 2), S-Detect had a higher area under receiver operating characteristic curve (AUC), accuracy and specificity (p <0.05). In comparison with the most experienced radiologist, the diagnostic accuracy and AUC were lower (p<0.05). In the less experienced radiologists, the diagnostic accuracy, specificity and AUC were significantly improved when combined with S-Detect (p<0.05), but not for experienced radiologists (radiologist 3 and 4) (p>0.05).Conclusions: S-Detect may become an additional diagnostic method for the diagnosis of thyroid nodules and improve the diagnostic performance of less experienced radiologists. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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