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Differing benefits of artificial intelligence-based computer-aided diagnosis for breast US according to workflow and experience level
- Title
- Differing benefits of artificial intelligence-based computer-aided diagnosis for breast US according to workflow and experience level
- Authors
- Lee S.E.; Han K.; Youk J.H.; Lee J.E.; Hwang J.-Y.; Rho M.; Yoon J.; Kim E.-K.; Yoon J.H.
- Ewha Authors
- 이지은
- SCOPUS Author ID
- 이지은
- Issue Date
- 2022
- Journal Title
- Ultrasonography
- ISSN
- 2288-5919
- Citation
- Ultrasonography vol. 41, no. 4, pp. 718 - 727
- Keywords
- Breast neoplasms; Diagnosis, Computer-assisted artificial intelligence; Ultrasonography
- Publisher
- Korean Society of Ultrasound in Medicine
- Indexed
- SCIE; SCOPUS; KCI
- Document Type
- Article
- 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).
- DOI
- 10.14366/usg.22014
- Appears in Collections:
- 의과대학 > 의학과 > Journal papers
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