ArticleInsights into imaging2023
Automatic and standardized quality assurance of digital mammography and tomosynthesis with deep convolutional neural networks.
Article in Insights into imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- AI performance varies considerably across mammography devices: a multi-site and multi-vendor retrospective study.Insights into imaging · 2026Article
- Landmark Regression with Attention U-Net for Assessing Breast Positioning Quality in MLO Mammography View.Diagnostics (Basel, Switzerland) · 2026Article
- AI-based image quality assessment of positioning in mammography: considerations and challenges.Insights into imaging · 2026Article
- Evaluation of the Diagnostic Accuracy of Comercially Available AI-CAD Solution in Mammography Screening in Mexican Women (Mammo-MX Database).Diagnostics (Basel, Switzerland) · 2026Article
- Enhancing breast positioning quality through real-time AI feedback.European radiology · 2026Article
- AI in 2D Mammography: Improving Breast Cancer Screening Accuracy.Medicina (Kaunas, Lithuania) · 2025Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe aim of this study was to develop and validate a commercially available AI platform for the automatic determination of image quality in mammography and tomosynthesis considering a standardized set of features. MATERIALS AND
methodsIn this retrospective study, 11,733 mammograms and synthetic 2D reconstructions from tomosynthesis of 4200 patients from two institutions were analyzed by assessing the presence of seven features which impact image quality in regard to breast positioning. Deep learning was applied to train five dCNN models on features detecting the presence of anatomical landmarks and three dCNN models for localization features. The validity of models was assessed by the calculation of the mean squared error in a test dataset and was compared to the reading by experienced radiologists.
resultsAccuracies of the dCNN models ranged between 93.0% for the nipple visualization and 98.5% for the depiction of the pectoralis muscle in the CC view. Calculations based on regression models allow for precise measurements of distances and angles of breast positioning on mammograms and synthetic 2D reconstructions from tomosynthesis. All models showed almost perfect agreement compared to human reading with Cohen's kappa scores above 0.9.
conclusionsAn AI-based quality assessment system using a dCNN allows for precise, consistent and observer-independent rating of digital mammography and synthetic 2D reconstructions from tomosynthesis. Automation and standardization of quality assessment enable real-time feedback to technicians and radiologists that shall reduce a number of inadequate examinations according to PGMI (Perfect, Good, Moderate, Inadequate) criteria, reduce a number of recalls and provide a dependable training platform for inexperienced technicians.
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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.