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Materials and methods used 22,621 mammograms from 8,825 women in a 10-year biennial two-reader program; 5,136 mammograms were analyzed after data-retrieval issues. AI assigned 1–100 scores, with ground truth from histopathology and thresholds set by Youden’s index. Results showed AUC 89.6%, sensitivity 72.38%, specificity 92.86%, and potential workload reduction with improved accuracy.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/diagnostic-capabilities-of-artificial-intelligence-as-an-additional-reader-in-a-breast-cancer-screening-program/333714/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/diagnostic-capabilities-of-artificial-intelligence-as-an-additional-reader-in-a-breast-cancer-screening-program/333714.png","ImageObject",300,407,{"name":92,"@type":93},"Asher","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What were the primary objectives of this AI evaluation?","Question",{"text":113,"@type":114},"The study aimed to assess AI’s early-detection capability during screening, focusing on interval cancers, AI-assisted detection using prior visits, and its effect on workload across different reading scenarios.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How was the AI system evaluated and what determined the decision threshold?",{"text":118,"@type":114},"AI scores from 1 to 100 were assigned to mammograms, with histopathology providing ground truth. Youden’s index established the optimal threshold.",{"name":120,"@type":111,"acceptedAnswer":121},"What were the key diagnostic performance results and the reported workflow impact?",{"text":122,"@type":114},"The AI achieved an AUC of 89.6%, with an optimal threshold of 30.44 yielding 72.38% sensitivity and 92.86% specificity. A hybrid triage workflow scenario suggested a 69.5% workload reduction and a 30.5% increase in accuracy.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},333714,1790186548,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},687197207639,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Seker et al. European Radiology (2024) 34:6145-6157 [https://doi.org/10.1007/s00330-024-10661-3](https://doi.org/10.1007/s00330-024-10661-3)  \nBREAST  \nDiagnostic capabilities of artificial  \nintelligence as an additional reader in a breast cancer screening program  \nMustafa Ege Seker1, Yilmaz Onat Koyluoglu1, Ayse Nilufer Ozaydin2, Sibel Ozkan Gurdal3, Beyza Ozcinar4, Neslihan Cabioglu4, Vahit Ozmen4 and Erkin Aribal1*  \nAbstract  \nObjectives We aimed to evaluate the early-detection capabilities of AI in a screening program over its duration, with a specific focus on the detection of interval cancers, the early detection of cancers with the assistance of AI from prior visits, and its impact on workload for various reading scenarios.  \nMaterials and methods The study included 22,621 mammograms of 8825 women within a 10-year biennial tworeader screening program. The statistical analysis focused on 5136 mammograms from 4282 women due to data retrieval issues, among whom 105 were diagnosed with breast cancer. The AI software assigned scores from 1 to 100. Histopathology results determined the ground truth, andYouden’s index was used to establish a threshold. Tumor characteristics were analyzed with ANOVA and chi-squared test, and different workflow scenarios were evaluated using bootstrapping.  \nResults The AI software achieved an AUC of 89 . 6%(86 .1–93. 2%, 95% CI) . The optimal threshold was 30.44, yielding 72. 38% sensitivity and 92. 86% specificity. Initially, AI identified 57 screening-detected cancers (83 . 82%), 15 interval cancers (51 . 72%), and 4 missed cancers (50%) . AI as a second reader could have led to earlier diagnosis in 24 patients (average 29.92 ± 19.67 months earlier) . No significant differences were found in cancer-characteristics groups. A hybrid triage workflow scenario showed a potential 69. 5% reduction in workload and a 30. 5% increase in accuracy. Conclusion This AI system exhibits high sensitivity and specificity in screening mammograms, effectively identifying interval and missed cancers and identifying 23% of cancers earlier in prior mammograms. Adopting AI as a triage mechanism has the potential to reduce workload by nearly 70% .  \nClinical relevance statement The study proposes a more efficient method for screening programs, both in terms of workload and accuracy.  \nKey Points  \n• Incorporating AI as a triage tool in screening workflow improves sensitivity (72.38%) and specificity (92.86%), enhancing detection rates for interval and missed cancers.  \n• AI-assisted triaging is effective in differentiating low and high-risk cases, reduces radiologist workload, and potentially enables broader screening coverage.  \nM. E. S. and Y. O. K. contributed equally to this work.  \n*Correspondence: Erkin Aribal[earibal@gmail.com](earibal@gmail.com)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/.)[.](http://creativecommons.org/licenses/by/4.0/.)  \n6146 Seker et al. European Radiology (2024) 34:6145-6157  \n• AI has the potential to facilitate early diagnosis compared to human reading","cbCaioLTF7b3Dhte","https://ap.wps.com/l/cbCaioLTF7b3Dhte","pdf",4225158,13,"English","# Abstract\n## Objectives\n## Materials and methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What were the primary objectives of this AI evaluation?\",\"answer\":\"The study aimed to assess AI’s early-detection capability during screening, focusing on interval cancers, AI-assisted detection using prior visits, and its effect on workload across different reading scenarios.\"},{\"question\":\"How was the AI system evaluated and what determined the decision threshold?\",\"answer\":\"AI scores from 1 to 100 were assigned to mammograms, with histopathology providing ground truth. Youden’s index established the optimal threshold.\"},{\"question\":\"What were the key diagnostic performance results and the reported workflow impact?\",\"answer\":\"The AI achieved an AUC of 89.6%, with an optimal threshold of 30.44 yielding 72.38% sensitivity and 92.86% specificity. A hybrid triage workflow scenario suggested a 69.5% workload reduction and a 30.5% increase in accuracy.\"}]","Diagnostic capabilities of artificial intelligence as an additional reader in a breast cancer screening program | PDF",1790003722,33]