{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T12:12:00Z","timestamp":1780315920664,"version":"3.54.1"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T00:00:00Z","timestamp":1698969600000},"content-version":"vor","delay-in-days":449,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,8,11]]},"abstract":"<jats:sec>\n                  <jats:title>Objectives:<\/jats:title>\n                  <jats:p>High breast density is a risk factor for breast cancer and overlapping of glandular tissue can mask lesions thus lowering mammographic sensitivity. Also, dense breasts are more vulnerable to increase recall rate and false-positive results. New generations of artificial intelligence (AI) have been introduced to the realm of mammography. We aimed to assess the strengths and challenges of adopting artificial intelligence in reading mammograms of dense breasts.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods:<\/jats:title>\n                  <jats:p>This study included 6600 mammograms of dense patterns \u201cc\u201d and \u201cd\u201d and presented 4061 breast abnormalities. All the patients were subjected to full-field digital mammography, breast ultrasound, and their mammographic images were scanned by AI software (Lunit INSIGHT MMG).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results:<\/jats:title>\n                  <jats:p>Diagnostic indices of the sono-mammography: a sensitivity of 98.71%, a specificity of 88.04%, a positive-predictive value of 80.16%, a negative-predictive value of 99.29%, and a diagnostic accuracy of 91.5%. AI-aided mammograms presented sensitivity of 88.29%, a specificity of 96.34%, a positive-predictive value of 92.2%, a negative-predictive value of 94.4%, and a diagnostic accuracy of 94.5% in its ability to read dense mammograms<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion:<\/jats:title>\n                  <jats:p>Dense breasts scanned with AI showed a notable reduction of mammographic misdiagnosis. Knowledge of such software challenges would enhance its application as a decision support tool to mammography in the diagnosis of cancer.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Advances in knowledge:<\/jats:title>\n                  <jats:p>Dense breast is challenging for radiologists and renders low sensitivity mammogram. Mammogram scanned by AI could be used to overcome such limitation, enhance the discrimination between benign and malignant breast abnormalities and the early detection of breast cancer.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1259\/bjro.20220018","type":"journal-article","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T19:24:46Z","timestamp":1658949886000},"source":"Crossref","is-referenced-by-count":3,"title":["Strengths and challenges of the artificial intelligence in the assessment of dense breasts"],"prefix":"10.1093","volume":"4","author":[{"given":"Sahar","family":"Mansour","sequence":"first","affiliation":[{"name":"Radiology, Kasr ElAiny Hospital, Cairo University , Cairo, Egypt"},{"name":"Baheya Foundation for Early Detection and Treatment of Breast Cancer , Giza, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Somia","family":"Soliman","sequence":"additional","affiliation":[{"name":"Pathology, Kasr ElAiny Hospital, Cairo University , Cairo, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abisha","family":"Kansakar","sequence":"additional","affiliation":[{"name":"Radiology, Kasr ElAiny Hospital, Cairo University , Cairo, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Marey","sequence":"additional","affiliation":[{"name":"FUJIFILM Medical Imaging and IT Solutions MEA , Dubai, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christiane","family":"Hunold","sequence":"additional","affiliation":[{"name":"FUJIFILM Medical Imaging and IT Solutions MEA , Dubai, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mennatallah Mohamed","family":"Hanafy","sequence":"additional","affiliation":[{"name":"Radiology, Kasr ElAiny Hospital, Cairo University , Cairo, Egypt"},{"name":"Baheya Foundation for Early Detection and Treatment of Breast Cancer , Giza, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,8,16]]},"reference":[{"key":"2024082313522234900_b1","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J Clin"},{"key":"2024082313522234900_b2","first-page":"219","article-title":"Artificial intelligence methods for the diagnosis of breast cancer by image processing: a review","volume":"10","author":"Sadoughi","year":"2018","journal-title":"Breast Cancer (Dove Med Press)"},{"key":"2024082313522234900_b3","doi-asserted-by":"crossref","first-page":"4687","DOI":"10.1002\/cncr.33138","article-title":"Mammographic breast density, its changes, and breast cancer risk in premenopausal and postmenopausal women","volume":"126","author":"Kim","year":"2020","journal-title":"Cancer"},{"key":"2024082313522234900_b4","doi-asserted-by":"crossref","first-page":"27","DOI":"10.21859\/mci-010212","article-title":"Investigating the factors affecting the mammographic density of breast tissue in patients referred to the breast cancer research center, iran","volume":"1","author":"Hashemi","journal-title":"Mcij"},{"key":"2024082313522234900_b5","doi-asserted-by":"crossref","DOI":"10.3332\/ecancer.2017.ed70","article-title":"Breast density and impacts on health","volume":"11","author":"Cruwys","year":"2017","journal-title":"Ecancermedicalscience"},{"key":"2024082313522234900_b6","article-title":"Impact of breast density awareness on knowledge about breast cancer risk factors and the self-perceived risk of breast cancer","volume":"10","author":"Bojanic","year":"2020","journal-title":"Diagnostics (Basel)"},{"key":"2024082313522234900_b7","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1148\/radiographics.15.4.7569138","article-title":"Critical pathways in using breast US","volume":"15","author":"Mendelson","year":"1995","journal-title":"Radiographics"},{"key":"2024082313522234900_b8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s43055-020-00268-1","article-title":"Comparative study between contrast-enhanced mammography, tomosynthesis, and breast ultrasound as complementary techniques to mammography in dense breast parenchyma","volume":"51","author":"Azzam","year":"2020","journal-title":"Egypt J Radiol Nucl Med"},{"key":"2024082313522234900_b9","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.semcancer.2020.06.002","article-title":"Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: state of the art","volume":"72","author":"Sechopoulos","year":"2021","journal-title":"Semin Cancer Biol"},{"key":"2024082313522234900_b10","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.breast.2019.12.014","article-title":"Stand-alone artificial intelligence - 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