Screening Mammography: Single Reading by One Radiologist With AI vs. Double Reading by Two Radiologists (AI-BCSQ)
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Group with AI iCAD version 3, Group without AI.
- Who it may be relevant to
- Registry conditions: Breast Cancer Screening, Artificial Intelligence (AI), Breast Cancer Screening and Diagnosis. Basic parameters: 45 years — 69 years · Female.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Czechia
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
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
Overview
A randomized prospective study comparing the evaluation of mammography images in a breast cancer screening programme by a single radiologist with AI support versus standard double reading by two radiologists without AI support.
Detailed description
In the intervention group, the first reading of the screening mammogram will be performed by one radiologist with AI support. After this AI-assisted first reading is completed and recorded for the study, a standard second reading will be carried out by another radiologist. This ensures that the legal requirement for double reading in the breast cancer screening program is maintained.
In the control group, mammograms will be evaluated according to the current standard practice - that is, independently by two radiologists without AI support.
Participants will be divided to the two groups based on a randomization scheme that determines specific days for evaluation with AI (Group 1) and without AI (Group 2). The randomization ensures equal distribution of weekdays between the two groups to minimize bias due to variability in daily workflow, diagnostic/screening ratios, or other operational factors.
Interventions
- Diagnostic test Group with AI iCAD version 3
Reading mammograms by one radiologist with AI support - Diagnostic test Group without AI
Standard double reading by two radiologists without AI.
Primary outcome measures
- Further assessment rate [Time frame: up to 190 days after screening mammmography]
Secondary outcome measures (2)
- Cancer Detection Rate [Time frame: up to 1 year after screening mammography]
- Recall Rate [Time frame: up to 190 days after screening mammography]
Eligibility criteria
Inclusion criteria
- age 45-69, asymptomatic woman participating in breast cancer screening programme
Exclusion criteria
- clinical signs of breast disease - indication for diagnostic mammography
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Open label
- Primary purpose
- Diagnostic
Study locations
Czechia · 1 center
- University Hospital Olomouc — Olomouc
Publications
- Larsen M, Olstad CF, Lee CI, Hovda T, Hoff SR, Martiniussen MA, Mikalsen KO, Lund-Hanssen H, Solli HS, Silberhorn M, Sulheim AO, Auensen S, Nygard JF, Hofvind S. Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway. Radiol Artif Intell. 2024 May;6(3):e230375. doi: 10.1148/ryai.230375. PMID 38597784
- Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue RTHM, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017 Dec;14(12):749-762. doi: 10.1038/nrclinonc.2017.141. Epub PMID 28975929
- Tudos Z, Veverkova L, Baxa J, Hartmann I, Ctvrtlik F. The current and upcoming era of radiomics in phaeochromocytoma and paraganglioma. Best Pract Res Clin Endocrinol Metab. 2025 Jan;39(1):101923. doi: 10.1016/j.beem.2024.101923. Epub 2024 Aug 23. PMID 39227277
- McDonald ES, Conant EF. Can AI Reduce the Harms of Screening Mammography? Radiol Artif Intell. 2023 Oct 25;5(6):e230304. doi: 10.1148/ryai.230304. eCollection 2023 Nov. No abstract available. PMID 38074781
- Letter H, Peratikos M, Toledano A, Hoffmeister J, Nishikawa R, Conant E, Shisler J, Maimone S, Diaz de Villegas H. Use of Artificial Intelligence for Digital Breast Tomosynthesis Screening: A Preliminary Real-world Experience. J Breast Imaging. 2023 May 22;5(3):258-266. doi: 10.1093/jbi/wbad015. PMID 38416890
- Dahlblom V, Dustler M, Tingberg A, Zackrisson S. Breast cancer screening with digital breast tomosynthesis: comparison of different reading strategies implementing artificial intelligence. Eur Radiol. 2023 May;33(5):3754-3765. doi: 10.1007/s00330-022-09316-y. Epub 2022 Dec 11. PMID 36502459
- Eisemann N, Bunk S, Mukama T, Baltus H, Elsner SA, Gomille T, Hecht G, Heywang-Kobrunner S, Rathmann R, Siegmann-Luz K, Tollner T, Vomweg TW, Leibig C, Katalinic A. Nationwide real-world implementation of AI for cancer detection in population-based mammography screening. Nat Med. 2025 Mar;31(3):917-924. doi: 10.1038/s41591-024-03408-6. Epub 2025 Jan 7. PMID 39775040
- Diaz O, Rodriguez-Ruiz A, Sechopoulos I. Artificial Intelligence for breast cancer detection: Technology, challenges, and prospects. Eur J Radiol. 2024 Jun;175:111457. doi: 10.1016/j.ejrad.2024.111457. Epub 2024 Apr 16. PMID 38640824
Identifiers
NCT: NCT07075679 · 03062025 · IGA_LF_2024_022