ArticleEuropean radiology2013
Standalone computer-aided detection compared to radiologists' performance for the detection of mammographic masses.
Article in European radiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy.BMJ (Clinical research ed.) · 2021Pooled it
- An Exploration of Discrepant Recalls Between AI and Human Readers of Malignant Lesions in Digital Mammography Screening.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence Detection of Missed Cancers at Digital Mammography That Were Detected at Digital Breast Tomosynthesis.Radiology. Artificial intelligence · 2021Article
- A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms.Journal of imaging · 2021Review
- [Applications of Artificial Intelligence in Mammography from a Development and Validation Perspective].Taehan Yongsang Uihakhoe chi · 2021Review
- AI-aided detection of malignant lesions in mammography screening - evaluation of a program in clinical practice.BJR open · 2021Article
- Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives.Radiology · 2019Review
- Focal breast lesion characterization according to the BI-RADS US lexicon: role of a computer-aided decision-making support.La Radiologia medica · 2018Article
- Detecting and classifying lesions in mammograms with Deep Learning.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesWe developed a computer-aided detection (CAD) system aimed at decision support for detection of malignant masses and architectural distortions in mammograms. The effect of this system on radiologists' performance depends strongly on its standalone performance. The purpose of this study was to compare the standalone performance of this CAD system to that of radiologists.
methodsIn a retrospective study, nine certified screening radiologists and three residents read 200 digital screening mammograms without the use of CAD. Performances of the individual readers and of CAD were computed as the true-positive fraction (TPF) at a false-positive fraction of 0.05 and 0.2. Differences were analysed using an independent one-sample t-test.
resultsAt a false-positive fraction of 0.05, the performance of CAD (TPF = 0.487) was similar to that of the certified screening radiologists (TPF = 0.518, P = 0.17). At a false-positive fraction of 0.2, CAD performance (TPF = 0.620) was significantly lower than the radiologist performance (TPF = 0.736, P <0.001). Compared to the residents, CAD performance was similar for all false-positive fractions.
conclusionsThe sensitivity of CAD at a high specificity was comparable to that of human readers. These results show potential for CAD to be used as an independent reader in breast cancer screening.
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