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Using screening data from 2004–2021 across ten breast centers in BreastScreen Norway, radiologists scored each case under an independent double-reading protocol while the AI generated NT and SN risk scores. Operating points N70 and P3 defined negative and positive cases, enabling analysis of misclassification and cancer detection behavior. Results covered 1,017,208 examinations and informed workload reduction and sensitivity implications.",{"@graph":69,"@context":122},[70,84,105],{"@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":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/retrospective-evaluation-of-a-ce-marked-ai-system-including-1017208-mammography-screening-examinations/370834/",{"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/retrospective-evaluation-of-a-ce-marked-ai-system-including-1017208-mammography-screening-examinations/370834.png","ImageObject",300,407,{"name":92,"@type":93},"McGucket","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What was the study objective in evaluating the CE-marked AI system?","Question",{"text":112,"@type":113},"To retrospectively evaluate how the CE-marked AI system performs in identifying breast cancer on screening mammograms, supporting safe and effective planning for prospective use.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were negative and positive cases defined using the AI scores?",{"text":117,"@type":113},"The AI assigned an NT score and an SN score. N70 selected cases among the 70% with the lowest NT score (defined as negative), and P3 selected cases among the 3% with the highest SN score (defined as positive).",{"name":119,"@type":110,"acceptedAnswer":120},"What do the results indicate about AI use in screening?",{"text":121,"@type":113},"At the operating points, the AI classified more than 80% of screen-detected cancers as positive when using the top 3% of AI risk scores. The authors conclude the AI performed well for identifying both negative cases and cancer cases, potentially reducing radiologist workload and increasing sensitivity.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},370834,1790832550,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},1236954412713,"https://us-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Hovda et al. European Radiology (2025) 35:5685–5694  \n[https://doi.org/10.1007/s00330-025-1](https://doi.org/10.1007/s00330-025-1)1521-4  \nBREAST Open Access  \nRetrospective evaluation of a CE-marked AI system, including 1,017,208  \nmammography screening examinations  \nTone Hovda 1, Marthe Larsen2, Marie Burns Bergan2, Jonas Gjesvik2, Lars A. Akslen3,4 and Solveig Hofvind2,5*   \nAbstract  \nObjectives To retrospectively evaluate the performance of a CE-marked AI system for identifying breast cancer on screening mammograms. Evidence from large retrospective studies is crucial for planning prospective studies and to further ensure safe implementation.  \nMaterials and methods We used data from screening examinations performed from 2004 to 2021 at ten breast centers in BreastScreen Norway. In the standard independent double reading setting, each radiologist scored each breast from 1 (negative) to 5 (high probability of cancer) . The AI system assigned each examination an NT and an SN score; the NT score aimed to classify examinations as negative with minimal misclassification while the SN score aimed to classify examinations as positive with high confidence. N70 was defined as being among the 70% with the lowest NT score and P3 was defined as being among the 3% with the highest SN score.  \nResults A total of 1,017,208 screening examinations were included in the study sample. At N70, 1 . 8%(107/5977) of the screen-detected and 34 . 5%(625/1812) of the interval cancers were defined as negative. Using P3 to define cases as positive, 81.5%(4871/5977) of the screen-detected and 19.0%(344/1812) of the interval cancers were defined as positive. Among the screen-detected cancers in N70, 11 . 2%(12/107) had an interpretation score > 2 by both radiologists.  \nConclusion The AI system performed well according to identifying negative cases and cancer cases. Thus, the AI system can be used to reduce workload for the radiologists and potentially increase the sensitivity of mammography.  \nKey Points  \nQuestion Results from large mammography screening samples not used in training AI algorithms are important to consider when planning prospective studies and implementation.  \nFindings More than 80% of the screening-detected cancers were classified as positive by AI when considering 3% of the examinations with the highest AI risk score as positive.  \nClinical relevance A lack of radiologists is a challenge in mammographic screening. Our findings support other studies that suggest the use of AI to reduce screen-reading workload.  \nKeywords Breast cancer, Mammography, Artificial intelligence, Screening  \n*Correspondence: Solveig Hofvind  \n[sshh@kreftregisteret. no](sshh@kreftregisteret. no)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2025 . 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/](http://creativecommons.org/)[ ](http://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/.)[.](licenses/by/4.0/.)  \nGraphical Abstract  \nIntroduction  \nBreast cancer is the most frequent cancer and among the leading causes of cancer deaths among women worldwide [1]. Mammographic screening is recommended as secondary prevention to reduce breast cancer m","cbCaifxti3Ne2Jmf","https://ap.wps.com/l/cbCaifxti3Ne2Jmf","pdf",1045485,"English","# Abstract\n# Introduction\n## Background and rationale\n## Screening benefits and harms\n## Role of AI in mammographic screening\n## Need for validation before implementation\n# Materials and methods\n# Results\n# Conclusion\n# Key Points","[{\"question\":\"What was the study objective in evaluating the CE-marked AI system?\",\"answer\":\"To retrospectively evaluate how the CE-marked AI system performs in identifying breast cancer on screening mammograms, supporting safe and effective planning for prospective use.\"},{\"question\":\"How were negative and positive cases defined using the AI scores?\",\"answer\":\"The AI assigned an NT score and an SN score. N70 selected cases among the 70% with the lowest NT score (defined as negative), and P3 selected cases among the 3% with the highest SN score (defined as positive).\"},{\"question\":\"What do the results indicate about AI use in screening?\",\"answer\":\"At the operating points, the AI classified more than 80% of screen-detected cancers as positive when using the top 3% of AI risk scores. The authors conclude the AI performed well for identifying both negative cases and cancer cases, potentially reducing radiologist workload and increasing sensitivity.\"}]","Retrospective evaluation of a CE-marked AI system, including 1,017,208 mammography screening examinations | PDF",1790190588,25]