ArticleEuropean radiology2023
Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- The translational chasm in machine learning for triple-negative breast cancer: a quantitative landscape assessment.Frontiers in oncology · 2026Pooled it
- Reading time in AI-assisted mammography: no measurable time penalty.European radiology · 2026Article
- AI software as a third reader in breast cancer screening-a prospective diagnostic observational study.European radiology · 2026Observational
- Bridging Pixels and Practice: AI-Assisted Mammography as a Next-Generation Diagnostic Strategy for Breast Cancer Screening.Diagnostics (Basel, Switzerland) · 2026Review
- Integrating artificial intelligence in healthcare: applications, challenges, and future directions.Future science OA · 2025Review
- Optimizing Artificial Intelligence Thresholds for Mammographic Lesion Detection: A Retrospective Study on Diagnostic Performance and Radiologist-Artificial Intelligence Discordance.Diagnostics (Basel, Switzerland) · 2025Article
- Current Strategies to Reducing Interval Breast Cancers: A Systematic Review.Breast cancer (Dove Medical Press) · 2025Review
- AI performance by mammographic density in a retrospective cohort study of 99,489 participants in BreastScreen Norway.European radiology · 2024Article
- [The possible benefit of artificial intelligence in an organized population-related screening program : Initial results and perspective].Radiologie (Heidelberg, Germany) · 2024Article
- In simple but challenging search tasks, most errors are stochastic.Attention, perception & psychophysics · 2024Article
- How do AI markings on screening mammograms correspond to cancer location? An informed review of 270 breast cancer cases in BreastScreen Norway.European radiology · 2024Article
- Enhancing Accuracy in Breast Density Assessment Using Deep Learning: A Multicentric, Multi-Reader Study.Diagnostics (Basel, Switzerland) · 2024Article
- Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway.Radiology. Artificial intelligence · 2024Article
- Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection.Breast cancer research : BCR · 2024Article
- Excluding specificity from research and development priorities is delaying the AI adoption in breast cancer screening.European radiology · 2023Article
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5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo compare results of selected performance measures in mammographic screening for an artificial intelligence (AI) system versus independent double reading by radiologists.
methodsIn this retrospective study, we analyzed data from 949 screen-detected breast cancers, 305 interval cancers, and 13,646 negative examinations performed in BreastScreen Norway during the period from 2010 to 2018. An AI system scored the examinations from 1 to 10, based on the risk of malignancy. Results from the AI system were compared to screening results after independent double reading. AI score 10 was set as the threshold. The results were stratified by mammographic density.
resultsA total of 92.7% of the screen-detected and 40.0% of the interval cancers had an AI score of 10. Among women with a negative screening outcome, 9.1% had an AI score of 10. For women with the highest breast density, the AI system scored 100% of the screen-detected cancers and 48.6% of the interval cancers with an AI score of 10, which resulted in a sensitivity of 80.9% for women with the highest breast density for the AI system, compared to 62.8% for independent double reading. For women with screen-detected cancers who had prior mammograms available, 41.9% had an AI score of 10 at the prior screening round.
conclusionsThe high proportion of cancers with an AI score of 10 indicates a promising performance of the AI system, particularly for women with dense breasts. Results on prior mammograms with AI score 10 illustrate the potential for earlier detection of breast cancers by using AI in screen-reading. KEY POINTS: • The AI system scored 93% of the screen-detected cancers and 40% of the interval cancers with AI score 10. • The AI system scored all screen-detected cancers and almost 50% of interval cancers among women with the highest breast density with AI score 10. • About 40% of the screen-detected cancers had an AI score of 10 on the prior mammograms, indicating a potential for earlier detection by using AI in screen-reading.
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