Comparing Radiologists' and Artificial Intelligence Performance Detecting Suspicious Microcalcifications on Screening Mammograms: A Pilot Cross-Sectional Study.

Sarah J Lewis, Jayden B Wells, Zhengqiang Jiang, Dania Abu Awwad, Melissa L Barron, Phuong Dung Yun Trieu

Abstract readComparative Study

In one paragraph

Article in Cancer control : journal of the Moffitt Cancer Center. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Sarah J LewisFaculty of Medicine and Health, The University of Sydney, NSW, Australia.

Jayden B WellsFaculty of Medicine and Health, The University of Sydney, NSW, Australia.

Zhengqiang JiangFaculty of Medicine and Health, The University of Sydney, NSW, Australia.

Dania Abu AwwadFaculty of Medicine and Health, The University of Sydney, NSW, Australia.ORCID 0000-0003-4327-0724

Melissa L BarronFaculty of Medicine and Health, The University of Sydney, NSW, Australia.

Phuong Dung Yun TrieuFaculty of Medicine and Health, The University of Sydney, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

IntroductionEarly breast cancer detection through periodic screening is crucial for reducing mortality due to later stage detection. Microcalcifications are common mammographic findings, present in both malignant lesions, benign pathologies, and normal tissues. This study aimed to assess radiologists' observer performance in determining suspicious calcifications requiring recall, and to compare reader performance to an in-house AI model trained and tested on Australian screening mammograms.MethodsIn this pilot proof-of-concept cross-sectional study, radiologists (n=27), breast physicians (n=2), and final year radiology trainees (n=6), completed the same mammographic test set consisting of 30 mammographic cases displaying different types of calcifications (10 breast cancer, 20 normal/benign). An in-house trained artificial intelligence model (

Indexed as

Artificial IntelligenceBreast NeoplasmsCalcinosisMammographyRadiologistsAustraliaClinical CompetenceCross-Sectional StudiesEarly Detection of CancerFemaleHumansMiddle AgedPilot Projectsartificial intelligencebreast cancermammographymicrocalcificationobserver performance

Identifiers

PMID42507819

DOI10.1177/10732748261470916

PMCPMC13408061

What OpenQuestion holds

Textmetadata

LicenceCC BY

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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.