ArticleAcademic radiology2010
Radiologists' perceptions of computer aided detection versus double reading for mammography interpretation.
Article in Academic radiology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 30 citations in OpenAlex.
- Service and clinical impacts of reader bias in breast cancer screening: a retrospective study.The British journal of radiology · 2024Article
- Mandating Limits on Workload, Duty, and Speed in Radiology.Radiology · 2022Review
- Improving radiologist's ability in identifying particular abnormal lesions on mammograms through training test set with immediate feedback.Scientific reports · 2021Article
- Computer-Aided Detection for Breast Cancer Screening in Clinical Settings: Scoping Review.JMIR medical informatics · 2019Article
- Breast cancer screening in England and the United States: a comparison of provision and utilisation.International journal of public health · 2015Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 7 institutions in 2 countries.
Funding
Abstract
RATIONALE AND
objectivesThe aim of this study was to examine radiologists' use and perceptions of computer-aided detection (CAD) and double reading for screening mammography interpretation. MATERIALS AND
methodsA mailed survey of 257 community radiologists participating in the national Breast Cancer Surveillance Consortium was used to assess perceptions and practices related to CAD and double reading. Latent class analysis was used to classify radiologists' overall perceptions of CAD and double reading on the basis of their agreement or disagreement with specific statements about CAD and double reading.
resultsMost radiologists (64%) reported using CAD for more than half the screening mammograms they interpreted, but only <5% reported double reading that much. More radiologists perceived that double reading improved cancer detection rates compared to CAD (74% vs 55% reported), whereas fewer radiologists thought that double reading decreased recall rates compared to CAD (50% vs 65% reported). Radiologists with the most favorable perceptions of CAD were more likely to think that CAD improved cancer detection rates without taking too much time compared to radiologists with the most unfavorable overall perceptions. In latent class analysis, an overall favorable perception of CAD was significantly associated with the use of CAD (81%), a higher percentage of workload in screening mammography (80%), academic affiliation (71%), and fellowship training (58%). Perceptions of double reading that were most favorable were associated with academic affiliation (98%).
conclusionsRadiologists' perceptions were more favorable toward double reading by a second clinician than by a computer, although fewer used double reading in their own practice. The majority of radiologists perceived both CAD and double reading at least somewhat favorably, although for largely different reasons.
Indexed as
Identifiers
What OpenQuestion holds
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.