ArticleRadiology. Artificial intelligence2021
A Multisite Study of a Breast Density Deep Learning Model for Full-Field Digital Mammography and Synthetic Mammography.
Article in Radiology. Artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review.BMC bioinformatics · 2023Pooled it
- Head-to-head comparisons of breast density assessment models using deep learning on digital and synthetic mammograms.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.MedComm · 2025Review
- Deep learning prediction of mammographic breast density using screening data.Scientific reports · 2025Article
- Automated Breast Density Assessment for Full-Field Digital Mammography and Digital Breast Tomosynthesis.Cancer prevention research (Philadelphia, Pa.) · 2025Article
- Artificial intelligence and consistency in patient care: a large-scale longitudinal study of mammographic density assessment.BJR artificial intelligence · 2025Article
- Inter- and intra-observer variability of qualitative visual breast-composition assessment in mammography among Japanese physicians: a first multi-institutional observer performance study in Japan.Breast cancer (Tokyo, Japan) · 2024Article
- Generalisable deep learning method for mammographic density prediction across imaging techniques and self-reported race.Communications medicine · 2024Article
- An Efficient Deep Neural Network to Classify Large 3D Images With Small Objects.IEEE transactions on medical imaging · 2024Article
- Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks.European radiology · 2023Article
- Automatic assessment of mammographic density using a deep transfer learning method.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis.JAMA network open · 2023Article
- The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review.Cancers · 2022Review
- Automatic Classification of Simulated Breast Tomosynthesis Whole Images for the Presence of Microcalcification Clusters Using Deep CNNs.Journal of imaging · 2022Article
- Mammographic Density Assessment by Artificial Intelligence-Based Computer-Assisted Diagnosis: A Comparison with Automated Volumetric Assessment.Journal of digital imaging · 2022Article
- Automatic estimation of knee effusion from limited MRI data.Scientific reports · 2022Article
- Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review.Breast cancer research : BCR · 2022Review
- When Doctors and AI Interact: on Human Responsibility for Artificial Risks.Philosophy & technology · 2022Article
- Precision Medicine: An Optimal Approach to Patient Care in Renal Cell Carcinoma.Frontiers in medicine · 2022Review
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop a Breast Imaging Reporting and Data System (BI-RADS) breast density deep learning (DL) model in a multisite setting for synthetic two-dimensional mammographic (SM) images derived from digital breast tomosynthesis examinations by using full-field digital mammographic (FFDM) images and limited SM data. MATERIALS AND
methodsA DL model was trained to predict BI-RADS breast density by using FFDM images acquired from 2008 to 2017 (site 1: 57 492 patients, 187 627 examinations, 750 752 images) for this retrospective study. The FFDM model was evaluated by using SM datasets from two institutions (site 1: 3842 patients, 3866 examinations, 14 472 images, acquired from 2016 to 2017; site 2: 7557 patients, 16 283 examinations, 63 973 images, 2015 to 2019). Each of the three datasets were then split into training, validation, and test. Adaptation methods were investigated to improve performance on the SM datasets, and the effect of dataset size on each adaptation method was considered. Statistical significance was assessed by using CIs, which were estimated by bootstrapping.
resultsWithout adaptation, the model demonstrated substantial agreement with the original reporting radiologists for all three datasets (site 1 FFDM: linearly weighted Cohen κ [κ
conclusionA BI-RADS breast density DL model demonstrated strong performance on FFDM and SM images from two institutions without training on SM images and improved by using few SM images.
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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.