Clinical trial · Observational
Artificial Intelligence in Breast Cancer Screening in Region Östergötland Linkoping
The Use of AI as a Third Reader and During Consensus in a Double Reading Breast Cancer Screening Program in Sweden
NCT05048095CI-TRIAL-00057944AI-ROLcompletedClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
The purpose of this observational study is to assess whether the use of AI (Transpara®) can lead to an improved quality of a double reading mammography screening program. This is investigated by performing AI as a third reader and as a decision support during the consensus meeting, compared with conventional mammography screening (double reading and consensus without AI).
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Breast Cancer | Malignant Breast Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| AI cancer detection system | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Screened women in Region Östergötland Linkoping
- interventionNames
- Other: AI cancer detection system
Primary outcomes (3)
- measure
- Cancer Detection rate
- timeFrame
- After 4 months of inclusion
- description
- Proportion of women diagnosed with breast cancer among those recalled after consensus
- measure
- Recall or referral rate
- timeFrame
- After 4 months of inclusion
- description
- Proportion of women who are referred for further diagnostic workup after consensus
- measure
- Positive predictive value of referrals
- timeFrame
Eligibility
Eligibility (as posted)
- Sex
- Female
- Minimum age
- 40 Years
- Maximum age
- 74 Years
Show eligibility criteria text
Inclusion Criteria: * Women participating in the regular Breast Cancer Screening Program in Region Östergötland Linkoping Exclusion Criteria: * Women with breast implants or other foreign implants in the mammogram * Women with symptoms or signs of suspected breast cancer
References
Publications (10)
- BACKGROUNDRodriguez-Ruiz A, Lang K, Gubern-Merida A, Broeders M, Gennaro G, Clauser P, Helbich TH, Chevalier M, Tan T, Mertelmeier T, Wallis MG, Andersson I, Zackrisson S, Mann RM, Sechopoulos I. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. J Natl Cancer Inst. 2019 Sep 1;111(9):916-922. doi: 10.1093/jnci/djy222. PMID 30834436
- BACKGROUNDRodriguez-Ruiz A, Krupinski E, Mordang JJ, Schilling K, Heywang-Kobrunner SH, Sechopoulos I, Mann RM. Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System. Radiology. 2019 Feb;290(2):305-314. doi: 10.1148/radiol.2018181371. Epub 2018 Nov 20. PMID 30457482
- BACKGROUNDvan Winkel SL, Rodriguez-Ruiz A, Appelman L, Gubern-Merida A, Karssemeijer N, Teuwen J, Wanders AJT, Sechopoulos I, Mann RM. Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study. Eur Radiol. 2021 Nov;31(11):8682-8691. doi: 10.1007/s00330-021-07992-w. Epub 2021 May 4. PMID 33948701
- BACKGROUNDPinto MC, Rodriguez-Ruiz A, Pedersen K, Hofvind S, Wicklein J, Kappler S, Mann RM, Sechopoulos I. Impact of Artificial Intelligence Decision Support Using Deep Learning on Breast Cancer Screening Interpretation with Single-View Wide-Angle Digital Breast Tomosynthesis. Radiology. 2021 Sep;300(3):529-536. doi: 10.1148/radiol.2021204432. Epub 2021 Jul 6. PMID 34227882
- BACKGROUNDRaya-Povedano JL, Romero-Martin S, Elias-Cabot E, Gubern-Merida A, Rodriguez-Ruiz A, Alvarez-Benito M. AI-based Strategies to Reduce Workload in Breast Cancer Screening with Mammography and Tomosynthesis: A Retrospective Evaluation. Radiology. 2021 Jul;300(1):57-65. doi: 10.1148/radiol.2021203555. Epub 2021 May 4. PMID 33944627
- BACKGROUNDLang K, Dustler M, Dahlblom V, Akesson A, Andersson I, Zackrisson S. Identifying normal mammograms in a large screening population using artificial intelligence. Eur Radiol. 2021 Mar;31(3):1687-1692. doi: 10.1007/s00330-020-07165-1. Epub 2020 Sep 2.