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Impact of different mammography systems on artificial intelligence performance in breast cancer screening
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dc.contributor.author | de Vries, Clarisse F. | |
dc.contributor.author | Colosimo, Samantha J. | |
dc.contributor.author | Staff, Roger T. | |
dc.contributor.author | Dymiter, Jaroslaw A. | |
dc.contributor.author | Yearsley, Joseph | |
dc.contributor.author | Dinneen, Deirdre | |
dc.contributor.author | Boyle, Moragh | |
dc.contributor.author | Harrison, David J. | |
dc.contributor.author | Anderson, Lesley A. | |
dc.contributor.author | Lip, Gerald | |
dc.contributor.author | Black, Corri | |
dc.contributor.author | Murray, Alison D. | |
dc.contributor.author | Wilde, Katie | |
dc.contributor.author | Blackwood, James D. | |
dc.contributor.author | Butterly, Claire | |
dc.contributor.author | Zurowski, John | |
dc.contributor.author | Eilbeck, Jon | |
dc.contributor.author | McSkimming, Colin | |
dc.date.accessioned | 2023-06-28T16:30:17Z | |
dc.date.available | 2023-06-28T16:30:17Z | |
dc.date.issued | 2023-05-01 | |
dc.identifier | 287794741 | |
dc.identifier | e130106f-ece9-4a1b-8256-ba83d0bea591 | |
dc.identifier | 85161367683 | |
dc.identifier.citation | de Vries , C F , Colosimo , S J , Staff , R T , Dymiter , J A , Yearsley , J , Dinneen , D , Boyle , M , Harrison , D J , Anderson , L A , Lip , G , Black , C , Murray , A D , Wilde , K , Blackwood , J D , Butterly , C , Zurowski , J , Eilbeck , J & McSkimming , C 2023 , ' Impact of different mammography systems on artificial intelligence performance in breast cancer screening ' , Radiology: Artificial Intelligence , vol. 5 , no. 3 , e220146 . https://doi.org/10.1148/ryai.220146 | en |
dc.identifier.other | RIS: urn:2B718C243D8FA3F3F4973768127D7D54 | |
dc.identifier.other | ORCID: /0000-0001-9041-9988/work/137088900 | |
dc.identifier.other | PubMedCentral: PMC10245180 | |
dc.identifier.uri | https://hdl.handle.net/10023/27836 | |
dc.description | Funding: Supported by the Industrical Centre of Artificial Intelligence Research in Digital Diagnosis (iCAIRD), which is funded by Innovate UK on behalf of UK Research and Innovation (UKRI) (project no. 104690). | en |
dc.description.abstract | Artificial intelligence (AI) tools may assist breast screening mammography programs, but limited evidence supports their generalizability to new settings. This retrospective study used a 3-year dataset (April 1, 2016-March 31, 2019) from a U.K. regional screening program. The performance of a commercially available breast screening AI algorithm was assessed with a prespecified and site-specific decision threshold to evaluate whether its performance was transferable to a new clinical site. The dataset consisted of women (aged approximately 50-70 years) who attended routine screening, excluding self-referrals, those with complex physical requirements, those who had undergone a previous mastectomy, and those who underwent screening that had technical recalls or did not have the four standard image views. In total, 55916 screening attendees (mean age, 60 years ± 6 [SD]) met the inclusion criteria. The prespecified threshold resulted in high recall rates (48.3%, 21929 of 45444), which reduced to 13.0% (5896 of 45444) following threshold calibration, closer to the observed service level (5.0%, 2774 of 55916). Recall rates also increased approximately threefold following a software upgrade on the mammography equipment, requiring per-software version thresholds. Using software-specific thresholds, the AI algorithm would have recalled 277 of 303 (91.4%) screen-detected cancers and 47 of 138 (34.1%) interval cancers. AI performance and thresholds should be validated for new clinical settings before deployment, while quality assurance systems should monitor AI performance for consistency. | |
dc.format.extent | 8 | |
dc.format.extent | 648156 | |
dc.language.iso | eng | |
dc.relation.ispartof | Radiology: Artificial Intelligence | en |
dc.subject | Breast | en |
dc.subject | Screening | en |
dc.subject | Mammography | en |
dc.subject | Computer applications: detection/diagnosis | en |
dc.subject | Neoplasms: primary | en |
dc.subject | Technology assessment | en |
dc.subject | RC0254 Neoplasms. Tumors. Oncology (including Cancer) | en |
dc.subject | 3rd-DAS | en |
dc.subject | SDG 3 - Good Health and Well-being | en |
dc.subject | MCC | en |
dc.subject.lcc | RC0254 | en |
dc.title | Impact of different mammography systems on artificial intelligence performance in breast cancer screening | en |
dc.type | Journal article | en |
dc.contributor.sponsor | Innovate UK | en |
dc.contributor.institution | University of St Andrews. School of Medicine | en |
dc.contributor.institution | University of St Andrews. Sir James Mackenzie Institute for Early Diagnosis | en |
dc.contributor.institution | University of St Andrews. Cellular Medicine Division | en |
dc.identifier.doi | 10.1148/ryai.220146 | |
dc.description.status | Peer reviewed | en |
dc.identifier.url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10245180/ | en |
dc.identifier.url | https://europepmc.org/article/MED/37293340 | en |
dc.identifier.grantnumber | TS/S013121/1 | en |
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