Artificial intelligence as a second reader for screening mammography

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class="basic-hero--publication__authors__spacer"></div> <div class="basic-hero--publication__authors__item"> Lin Yang </div> <div class="basic-hero--publication__authors__spacer"></div> <div class="basic-hero--publication__authors__item"> Shawn Xu </div> <div class="basic-hero--publication__authors__spacer"></div> <div class="basic-hero--publication__authors__item"> Christopher Kelly </div> <div class="basic-hero--publication__authors__spacer"></div> <div class="basic-hero--publication__authors__item"> Timo Kohlberger </div> <div class="basic-hero--publication__authors__spacer"></div> <div class="basic-hero--publication__authors__item"> Daniel Golden </div> <div class="basic-hero--publication__authors__spacer"></div> <div class="basic-hero--publication__authors__item"> Akib Uddin </div> <div class="basic-hero--publication__authors__spacer"></div> <div class="basic-hero--publication__authors__item"> <a href="/people/107467/">Joe Ledsam</a> </div> <div class="basic-hero--publication__authors__spacer"></div> <div class="basic-hero--publication__authors__item"> Radiology Advances, 1(2) (2024) </div> </div> <div class="basic-hero--publication__buttons"> <a class="glue-button glue-button--high-emphasis glue-button--reversed glue-button--icon" href="https://academic.oup.com/radadv/article/1/2/umae011/7667296" target="_blank" rel="noreferrer noopener"> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#file-download"></use> </svg> <span class="js-gt-item-id">Download</span> </a> <a class="glue-button glue-button--medium-emphasis glue-button--reversed" href="https://scholar.google.com/scholar?lr&ie=UTF-8&oe=UTF-8&q=Artificial intelligence as a second reader for screening mammography Etsuji Nakai Alessandro Scoccia Pappagallo Hiroki Kayama Lin Yang Shawn Xu Christopher Kelly Timo Kohlberger Daniel Golden Akib Uddin Joe Ledsam" target="_blank" rel="noreferrer noopener"> <span class="js-gt-item-id">Google Scholar</span> </a> <div class="glue-copy" glue-copy-success="copied!" glue-copy-fail="Press Ctrl+C or ⌘+C to copy"> <input class="glue-copy-value" type="text" value="@article{53382, title = {Artificial intelligence as a second reader for screening mammography}, author = {Etsuji Nakai and Alessandro Scoccia Pappagallo and Hiroki Kayama and Lin Yang and Shawn Xu and Christopher Kelly and Timo Kohlberger and Daniel Golden and Akib Uddin and Joe Ledsam}, year = {2024}, URL = {https://academic.oup.com/radadv/article/1/2/umae011/7667296}, journal = {Radiology Advances}, volume = {1(2)} } " aria-label="URL" readonly> <button class="glue-button glue-button--medium-emphasis glue-button--reversed glue-copy-button" aria-live='polite'> <span class="js-gt-item-id">Copy Bibtex</span> </button> </div> </div> </div> </div> </div> </section> <section class="offset-two-up --theme-light"> <div class="glue-page glue-grid"> <div class="offset-two-up__left-col glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-3-lg"> <h3 class="offset-two-up__headline headline-3">Abstract</h3> </div> <div class="glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-9-lg"> Background<br>Artificial intelligence (AI) has shown promise in mammography interpretation, and its use as a second reader in breast cancer screening may reduce the burden on health care systems.<br><br>Purpose<br>To evaluate the performance differences between routine double read and an AI as a second reader workflow (AISR), where the second reader is replaced with AI.<br><br>Materials and Methods<br>A cohort of patients undergoing routine breast cancer screening at a single center with mammography was retrospectively collected between 2005 and 2021. A model developed on US and UK data was fine-tuned on Japanese data. We subsequently performed a reader study with 10 qualified readers with varied experience (5 reader pairs), comparing routine double read to an AISR workflow.<br><br>Results<br>A “test set” of 4,059 women (mean age, 56 ± 14 years; 157 positive, 3,902 negative) was collected, with 278 (mean age 55 ± 13 years; 90 positive, 188 negative) evaluated for the reader study. We demonstrate an area under the curve =.84 (95% confidence interval [CI], 0.805-0.881) on the test set, with no significant difference to decisions made in clinical practice (P = .32). Compared with routine double reading, in the AISR arm, sensitivity improved by 7.6% (95% CI, 3.80-11.4; P = .00004) and specificity decreased 3.4% (1.42-5.43; P = .0016), with 71% (212/298) of scans no longer requiring input from a second reader. Variation in recall decision between reader pairs improved from a Cohen kappa of κ = .65 (96% CI, 0.61-0.68) to κ = .74 (96% CI, 0.71-0.77) in the AISR arm. </div> </div> </section> <section class="offset-two-up --theme-light"> <div class="glue-page glue-grid"> <div class="offset-two-up__left-col glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-3-lg"> <h3 class="offset-two-up__headline headline-3">Research Areas</h3> </div> <div class="glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-9-lg"> <ul class="card-stack--small nested-glue-grid-override" data-gt-id="research_areas_small_card_stack" data-gt-component-name="Resarch Areas - Small Card Stack"> <li class="glue-grid__col glue-grid__col--span-6-md glue-grid__col--span-4-sm"> <a class="glue-card not-glue glue-card--small" href="https://research.google/research-areas/machine-intelligence/"> <div class="glue-card__inner"> <div class="glue-card__content"> <div 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Artificial intelligence as a second reader for screening mammography