{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T23:50:55Z","timestamp":1785887455210,"version":"3.56.0"},"reference-count":33,"publisher":"SAGE Publications","issue":"8","license":[{"start":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T00:00:00Z","timestamp":1781049600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Governmental funding for clinical research"},{"name":"Strategic research area eSSENCE"},{"name":"Analytic Imaging Diagnostics Arena (AIDA), Vinnova","award":["2021-01420"],"award-info":[{"award-number":["2021-01420"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Acta Radiol"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Breast cancer diagnoses are limited in low- and middle-income settings due to lack of medical resources. In these settings, point-of-care ultrasound (POCUS) combined with artificial intelligence (AI)-based interpretation could be a suitable approach.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To compare the performance of an AI-based breast cancer classification algorithm with radiologists and assess whether POCUS performs comparably to standard breast ultrasound (BUS) as a stand-alone imaging technique.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Material and Methods<\/jats:title>\n                    <jats:p>A total of 70 POCUS and 70 case-matched BUS images (11 malignant, 21 benign, 38 normal) from 40 women (mean age=50.3\u2009\u00b1\u200916.65) were interpreted by four breast radiologists in a multi-reader, multi-case setup. Readers rated risk of malignancy on single images on a 5-point scale similar to BI-RADS (\u22653 considered positive, i.e. malignant). An in-house\u2013developed AI-based algorithm also analyzed the images. The breast cancer detection performance for all modalities was assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>On BUS, AI and radiologists performed comparably (AUC=0.98 [95% confidence interval (CI)=0.93\u20131.00] vs. 0.97 [95% CI=0.93\u20131.00]; sensitivity 1.00 vs. 1.00; specificity 0.75 vs. 0.78). The performance was similar on POCUS, for both AI and radiologists (AUC 0.99 [95% CI=0.98\u20131.00] vs. 0.99 [95% CI=0.96\u20131.00]; sensitivity 1.00 vs. 1.00; specificity 0.92 vs. 0.77). No statistically significant differences were observed between BUS and POCUS or radiologists and AI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study demonstrates the potential of reliable AI-based breast cancer detection, both in standard ultrasound imaging and in POCUS imaging.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/02841851261454509","type":"journal-article","created":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T13:44:39Z","timestamp":1781099079000},"page":"653-662","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring artificial intelligence in point-of-care and standard breast ultrasound: a paired reader study"],"prefix":"10.1177","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6873-5332","authenticated-orcid":false,"given":"Marisa","family":"Wodrich","sequence":"first","affiliation":[{"name":"Lund University"},{"name":"Section for Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. 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