{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T10:09:51Z","timestamp":1790762991217,"version":"4.1.0"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"1128","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BJR"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:sec><jats:title>Objective:<\/jats:title><jats:p> To study the impact of artificial intelligence (AI) on the performance of mammogram with regard to the classification of the detected breast lesions in correlation to ultrasound-aided mammograms. <\/jats:p><\/jats:sec><jats:sec><jats:title>Methods:<\/jats:title><jats:p> Ethics committee approval was obtained in this prospective analysis. The study included 2000 mammograms. The mammograms were interpreted by the radiologists and breast ultrasound was performed for all cases. The Breast Imaging Reporting and Data System (BI-RADS) score was applied regarding the combined evaluation of the mammogram and the ultrasound modalities. Each breast side was individually assessed with the aid of AI scanning in the form of targeted heat-map and then, a probability of malignancy (abnormality scoring percentage) was obtained. Operative and the histopathology data were the standard of reference. <\/jats:p><\/jats:sec><jats:sec><jats:title>Results:<\/jats:title><jats:p> Normal assigned cases (BI-RADS 1) with no lesions were excluded from the statistical evaluation. The study included 538 benign and 642 malignant breast lesions (n = 1180, 59%). BI-RADS categories for the breast lesions with regard to the combined evaluation of the digital mammogram and ultrasound were assigned BI-RADS 2 (Benign) in 385 lesions with AI median value of the abnormality scoring percentage of 10 (n = 385\/1180, 32.6%), and BI-RADS 5 (malignant) in 471, that had showed median percentage AI value of 88 (n = 471\/1180, 39.9%). AI abnormality scoring of 59% yielded a sensitivity of 96.8% and specificity of 90.1% in the discrimination of the breast lesions detected on the included mammograms. <\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion:<\/jats:title><jats:p> AI could be considered as an optional primary reliable complementary tool to the digital mammogram for the evaluation of the breast lesions. The color hue and the abnormality scoring percentage presented a credible method for the detection and discrimination of breast cancer of near accuracy to the breast ultrasound. So consequently, AI- mammogram combination could be used as a one setting method to discriminate between cases that require further imaging or biopsy from those that need only time interval follows up. <\/jats:p><\/jats:sec><jats:sec><jats:title>Advances in knowledge:<\/jats:title><jats:p> Recently, the indulgence of AI in the work-up of breast cancer was concerned. AI noted as a screening strategy for the detection of breast cancer. In the current work, the performance of AI was studied with regard to the diagnosis not just the detection of breast cancer in the mammographic-detected breast lesions. The evaluation was concerned with AI as a possible complementary reading tool to mammogram and included the qualitative assessment of the color hue and the quantitative integration of the abnormality scoring percentage. <\/jats:p><\/jats:sec>","DOI":"10.1259\/bjr.20210820","type":"journal-article","created":{"date-parts":[[2021,10,6]],"date-time":"2021-10-06T20:11:49Z","timestamp":1633551109000},"source":"Crossref","is-referenced-by-count":17,"title":["Can artificial intelligence replace ultrasound as a complementary tool to mammogram for the diagnosis of the breast cancer?"],"prefix":"10.1093","volume":"94","author":[{"given":"Sahar","family":"Mansour","sequence":"first","affiliation":[{"name":"Women\u2019s Imaging Unit \u2013 Kasr El Ainy Hospital- Cairo University, Giza, Egypt"},{"name":"Department of Radiology, Baheya Foundation for Early Detection and Treatment of Breast Cancer, Giza, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rasha","family":"Kamal","sequence":"additional","affiliation":[{"name":"Women\u2019s Imaging Unit \u2013 Kasr El Ainy Hospital- Cairo University, Giza, Egypt"},{"name":"Department of Radiology, Baheya Foundation for Early Detection and Treatment of Breast Cancer, Giza, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lamiaa","family":"Hashem","sequence":"additional","affiliation":[{"name":"Women\u2019s Imaging Unit \u2013 Kasr El Ainy Hospital- Cairo University, Giza, Egypt"},{"name":"Department of Radiology, Baheya Foundation for Early Detection and Treatment of Breast Cancer, Giza, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Basma","family":"AlKalaawy","sequence":"additional","affiliation":[{"name":"Women\u2019s Imaging Unit \u2013 Kasr El Ainy Hospital- Cairo University, Giza, Egypt"},{"name":"Department of Radiology, Baheya Foundation for Early Detection and Treatment of Breast Cancer, Giza, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","reference":[{"key":"b1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2251011667"},{"key":"b2","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.209.2.9807581"},{"key":"b3","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2019182627"},{"key":"b4","first-page":"1","author":"Wang D","year":"2016","journal-title":"Beth Israel Deaconess Medical Center, Harvard Medical School"},{"key":"b5","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018182404"},{"key":"b6","first-page":"2563","volume":"1","author":"Nam H","year":"2018","journal-title":"Adv Neur In"},{"key":"b7","first-page":"1520","volume":"1","author":"Noh H","year":"2015","journal-title":"Proc IEEE I Conf Comp Vis"},{"key":"b8","author":"D\u2019Orsi CJ","year":"2013","journal-title":"Reston, VA, American College of Radiology"},{"key":"b9","doi-asserted-by":"publisher","DOI":"10.2147\/BCTT.S175311"},{"key":"b10","doi-asserted-by":"publisher","DOI":"10.1001\/jama.299.18.2151"},{"key":"b11","doi-asserted-by":"publisher","DOI":"10.1016\/j.carj.2018.02.002"},{"key":"b12","doi-asserted-by":"publisher","DOI":"10.1136\/svn-2017-000101"},{"key":"b13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2015.02.001"},{"key":"b14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2018.02.032"},{"key":"b16","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-017-4835-7"},{"key":"b17","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.18.20532"},{"key":"b18","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-011-9813-z"},{"key":"b19","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-017-4835-7"},{"key":"b20","doi-asserted-by":"publisher","DOI":"10.1148\/rg.306105511"},{"key":"b21","volume":"941605","author":"Buelow T","year":"2019","journal-title":"Bellingham, Wash: International Society for Optics and Photonics"}],"container-title":["The British Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.birpublications.org\/doi\/pdf\/10.1259\/bjr.20210820","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T16:42:39Z","timestamp":1705077759000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bjr\/article\/7451709"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12]]},"references-count":20,"journal-issue":{"issue":"1128","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["10.1259\/bjr.20210820"],"URL":"https:\/\/doi.org\/10.1259\/bjr.20210820","relation":{},"ISSN":["0007-1285","1748-880X"],"issn-type":[{"value":"0007-1285","type":"print"},{"value":"1748-880X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12]]},"article-number":"20210820"}}