Dataset Information

Use of Artificial Intelligence for Reducing Unnecessary Recalls at Screening Mammography: A Simulation Study.

ABSTRACT:

Objective

To conduct a simulation study to determine whether artificial intelligence (AI)-aided mammography reading can reduce unnecessary recalls while maintaining cancer detection ability in women recalled after mammography screening.

Materials and methods

A retrospective reader study was performed by screening mammographies of 793 women (mean age ± standard deviation, 50 ± 9 years) recalled to obtain supplemental mammographic views regarding screening mammography-detected abnormalities between January 2016 and December 2019 at two screening centers. Initial screening mammography examinations were interpreted by three dedicated breast radiologists sequentially, case by case, with and without AI aid, in a single session. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and recall rate for breast cancer diagnosis were obtained and compared between the two reading modes.

Results

Fifty-four mammograms with cancer (35 invasive cancers and 19 ductal carcinomas in situ) and 739 mammograms with benign or negative findings were included. The reader-averaged AUC improved after AI aid, from 0.79 (95% confidence interval [CI], 0.74-0.85) to 0.89 (95% CI, 0.85-0.94) (p < 0.001). The reader-averaged specificities before and after AI aid were 41.9% (95% CI, 39.3%-44.5%) and 53.9% (95% CI, 50.9%-56.9%), respectively (p < 0.001). The reader-averaged sensitivity was not statistically different between AI-unaided and AI-aided readings: 89.5% (95% CI, 83.1%-95.9%) vs. 92.6% (95% CI, 86.2%-99.0%) (p = 0.053), although the sensitivities of the least experienced radiologists before and after AI aid were 79.6% (43 of 54 [95% CI, 66.5%-89.4%]) and 90.7% (49 of 54 [95% CI, 79.7%-96.9%]), respectively (p = 0.031). With AI aid, the reader-averaged recall rate decreased by from 60.4% (95% CI, 57.8%-62.9%) to 49.5% (95% CI, 46.5%-52.4%) (p < 0.001).

Conclusion

AI-aided reading reduced the number of recalls and improved the diagnostic performance in our simulation using women initially recalled for supplemental mammographic views after mammography screening.

SUBMITTER: Kim YS

PROVIDER: S-EPMC9747265 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Use of Artificial Intelligence for Reducing Unnecessary Recalls at Screening Mammography: A Simulation Study.

Kim Yeon Soo YS Jang Myoung-Jin MJ Lee Su Hyun SH Kim Soo-Yeon SY Ha Su Min SM Kwon Bo Ra BR Moon Woo Kyung WK Chang Jung Min JM

Korean journal of radiology 20221201 12

<h4>Objective</h4>To conduct a simulation study to determine whether artificial intelligence (AI)-aided mammography reading can reduce unnecessary recalls while maintaining cancer detection ability in women recalled after mammography screening.<h4>Materials and methods</h4>A retrospective reader study was performed by screening mammographies of 793 women (mean age ± standard deviation, 50 ± 9 years) recalled to obtain supplemental mammographic views regarding screening mammography-detected abnor ...[more]

PMID: 36447412

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Dataset Information

Use of Artificial Intelligence for Reducing Unnecessary Recalls at Screening Mammography: A Simulation Study.

Objective

Materials and methods

Results

Conclusion

Publications

Use of Artificial Intelligence for Reducing Unnecessary Recalls at Screening Mammography: A Simulation Study.

Similar Datasets

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