Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이지은 | * |
dc.date.accessioned | 2022-11-03T16:30:59Z | - |
dc.date.available | 2022-11-03T16:30:59Z | - |
dc.date.issued | 2022 | * |
dc.identifier.issn | 2288-5919 | * |
dc.identifier.other | OAK-32509 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/262842 | - |
dc.description.abstract | Purpose: This study evaluated how artificial intelligence-based computer-assisted diagnosis (AI-CAD) for breast ultrasonography (US) influences diagnostic performance and agreement between radiologists with varying experience levels in different workflows. Methods: Images of 492 breast lesions (200 malignant and 292 benign masses) in 472 women taken from April 2017 to June 2018 were included. Six radiologists (three inexperienced [<1 year of experience] and three experienced [10-15 years of experience]) individually reviewed US images with and without the aid of AI-CAD, first sequentially and then simultaneously. Diagnostic performance and interobserver agreement were calculated and compared between radiologists and AI-CAD. Results: After implementing AI-CAD, the specificity, positive predictive value (PPV), and accuracy significantly improved, regardless of experience and workflow (all P<0.001, respectively). The overall area under the receiver operating characteristic curve significantly increased in simultaneous reading, but only for inexperienced radiologists. The agreement for Breast Imaging Reporting and Database System (BI-RADS) descriptors generally increased when AI-CAD was used (κ=0.29-0.63 to 0.35-0.73). Inexperienced radiologists tended to concede to AI-CAD results more easily than experienced radiologists, especially in simultaneous reading (P<0.001). The conversion rates for final assessment changes from BI-RADS 2 or 3 to BI-RADS higher than 4a or vice versa were also significantly higher in simultaneous reading than sequential reading (overall, 15.8% and 6.2%, respectively; P<0.001) for both inexperienced and experienced radiologists. Conclusion: Using AI-CAD to interpret breast US improved the specificity, PPV, and accuracy of radiologists regardless of experience level. AI-CAD may work better in simultaneous reading to improve diagnostic performance and agreement between radiologists, especially for inexperienced radiologists. © 2022 Korean Society of Ultrasound in Medicine (KSUM). | * |
dc.language | English | * |
dc.publisher | Korean Society of Ultrasound in Medicine | * |
dc.subject | Breast neoplasms | * |
dc.subject | Diagnosis, Computer-assisted artificial intelligence | * |
dc.subject | Ultrasonography | * |
dc.title | Differing benefits of artificial intelligence-based computer-aided diagnosis for breast US according to workflow and experience level | * |
dc.type | Article | * |
dc.relation.issue | 4 | * |
dc.relation.volume | 41 | * |
dc.relation.index | SCIE | * |
dc.relation.index | SCOPUS | * |
dc.relation.index | KCI | * |
dc.relation.startpage | 718 | * |
dc.relation.lastpage | 727 | * |
dc.relation.journaltitle | Ultrasonography | * |
dc.identifier.doi | 10.14366/usg.22014 | * |
dc.identifier.scopusid | 2-s2.0-85139515855 | * |
dc.author.google | Lee S.E. | * |
dc.author.google | Han K. | * |
dc.author.google | Youk J.H. | * |
dc.author.google | Lee J.E. | * |
dc.author.google | Hwang J.-Y. | * |
dc.author.google | Rho M. | * |
dc.author.google | Yoon J. | * |
dc.author.google | Kim E.-K. | * |
dc.author.google | Yoon J.H. | * |
dc.contributor.scopusid | 이지은(35746498800) | * |
dc.date.modifydate | 20240301081003 | * |