ArticleEuropean radiology2023
Breast cancer screening with digital breast tomosynthesis: comparison of different reading strategies implementing artificial intelligence.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07075679 (Use of Artificial Intelligence in Breast Cancer Screening), which is not on this map. Cited by 19 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Use of Artificial Intelligence in Breast Cancer Screening: Impact of AI-Assisted Single Reading by One Radiologist on Screening Quality Indicators Compared to Standard Double Reading by Two Radiologists Without AI
Who cites it
19 citing papers in PubMed, 40 citations in OpenAlex.
- Artificial intelligence in breast cancer screening: A systematic review and meta-analysis of integration strategies.European journal of radiology open · 2026Review
- AI-based triage and decision support in mammography and digital tomosynthesis for breast cancer screening: a paired, noninferiority trial.Nature medicine · 2026Article
- In Silico Digital Breast Tomosynthesis Dataset for the Comparative Analysis of Deep Learning Models in Tumor Segmentation.Journal of imaging informatics in medicine · 2026Article
- Assessing the robustness of AI lesion risk scores at different exposure settings using an anthropomorphic breast phantom.Radiation protection dosimetry · 2026Article
- Integrating artificial intelligence in healthcare: applications, challenges, and future directions.Future science OA · 2025Review
- Workload reduction of digital breast tomosynthesis screening using artificial intelligence and synthetic mammography: a simulation study.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- External Validation of a Commercial Artificial Intelligence Algorithm on a Diverse Population for Detection of False Negative Breast Cancers.Journal of breast imaging · 2025Article
- Mammography Based Nomogram Integrating Radiomics and Clinical Features to Predict Benign or Malignant Regression of BI-RADS 4A Lesions at Follow-Up.International journal of women's health · 2025Article
- Our journey toward implementation of digital breast tomosynthesis in breast cancer screening: the Malmö Breast Tomosynthesis Screening Project.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- In simple but challenging search tasks, most errors are stochastic.Attention, perception & psychophysics · 2024Article
- Effects of artificial intelligence implementation on efficiency in medical imaging-a systematic literature review and meta-analysis.NPJ digital medicine · 2024Review
- Artificial Intelligence (AI)-Based Computer-Assisted Detection and Diagnosis for Mammography: An Evidence-Based Review of Food and Drug Administration (FDA)-Cleared Tools for Screening Digital Breast Tomosynthesis (DBT).AI in precision oncology · 2024Review
- AI in breast cancer screening: a critical overview of what we know.European radiology · 2024Article
- AI-enhanced Mammography With Digital Breast Tomosynthesis for Breast Cancer Detection: Clinical Value and Comparison With Human Performance.Radiology. Imaging cancer · 2024Article
- Optimising the diagnostic accuracy of First post-contrAst SubtracTed breast MRI (FAST MRI) through interpretation-training: a multicentre e-learning study, mapping the learning curve of NHS Breast Screening Programme (NHSBSP) mammogram readers using an enriched dataset.Breast cancer research : BCR · 2024Article
- Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway.Radiology. Artificial intelligence · 2024Article
- Artificial Intelligence in Senology - Where Do We Stand and What Are the Future Horizons?European journal of breast health · 2024Article
- To think it, wish it, even want it-but do it! Quality control measures in mammography image interpretation: radiologists' attitudes and preferences vs perceived obstacles and limitations.European radiology · 2023Article
- Personalized breast cancer screening with selective addition of digital breast tomosynthesis through artificial intelligence.Journal of medical imaging (Bellingham, Wash.) · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesDigital breast tomosynthesis (DBT) can detect more cancers than the current standard breast screening method, digital mammography (DM); however, it can substantially increase the reading workload and thus hinder implementation in screening. Artificial intelligence (AI) might be a solution. The aim of this study was to retrospectively test different ways of using AI in a screening workflow.
methodsAn AI system was used to analyse 14,772 double-read single-view DBT examinations from a screening trial with paired DM double reading. Three scenarios were studied: if AI can identify normal cases that can be excluded from human reading; if AI can replace the second reader; if AI can replace both readers. The number of detected cancers and false positives was compared with DM or DBT double reading.
resultsBy excluding normal cases and only reading 50.5% (7460/14,772) of all examinations, 95% (121/127) of the DBT double reading detected cancers could be detected. Compared to DM screening, 27% (26/95) more cancers could be detected (p < 0.001) while keeping recall rates at the same level. With AI replacing the second reader, 95% (120/127) of the DBT double reading detected cancers could be detected-26% (25/95) more than DM screening (p < 0.001)-while increasing recall rates by 53%. AI alone with DBT has a sensitivity similar to DM double reading (p = 0.689).
conclusionAI can open up possibilities for implementing DBT screening and detecting more cancers with the total reading workload unchanged. Considering the potential legal and psychological implications, replacing the second reader with AI would probably be most the feasible approach. KEY POINTS: • Breast cancer screening with digital breast tomosynthesis and artificial intelligence can detect more cancers than mammography screening without increasing screen-reading workload. • Artificial intelligence can either exclude low-risk cases from double reading or replace the second reader. • Retrospective study based on paired mammography and digital breast tomosynthesis screening data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.