ReviewBJR open2022
Deep learning in breast imaging.
Review in BJR open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 32 citations in OpenAlex.
- Zero-Shot Domain Adaptation of 2D Mammography Lesion Detection Model for 3D Breast CT Malignancy Detection.Cancers · 2026Article
- High-Performance Open-Source AI for Breast Cancer Detection and Localization in MRI.Radiology. Artificial intelligence · 2025Article
- The Potential Diagnostic Application of Artificial Intelligence in Breast Cancer.Current pharmaceutical design · 2025Review
- Real-time AI-guided ultrasound localization method for breast tumor rotational resection.Frontiers in oncology · 2025Article
- Article
- The Challenge of Deep Learning for the Prevention and Automatic Diagnosis of Breast Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Predictive Modeling of Long-Term Survivors with Stage IV Breast Cancer Using the SEER-Medicare Dataset.Cancers · 2024Article
- AI-enhanced Mammography With Digital Breast Tomosynthesis for Breast Cancer Detection: Clinical Value and Comparison With Human Performance.Radiology. Imaging cancer · 2024Article
- Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Comparison of deep-learning and radiomics-based machine-learning methods for the identification of chronic obstructive pulmonary disease on low-dose computed tomography images.Quantitative imaging in medicine and surgery · 2024Article
- LowQuantitative imaging in medicine and surgery · 2024Article
- Development of a nomogram-based model combining intra- and peritumoral ultrasound radiomics with clinical features for differentiating benign from malignant in Breast Imaging Reporting and Data System category 3-5 nodules.Quantitative imaging in medicine and surgery · 2023Article
- Deep learning radiomics for prediction of axillary lymph node metastasis in patients with clinical stage T1-2 breast cancer.Quantitative imaging in medicine and surgery · 2023Article
- Genetic Factors in the Screening and Imaging for Breast Cancer.Korean journal of radiology · 2023Review
- Review
- Review
- Identification of ocular refraction based on deep learning algorithm as a novel retinoscopy method.Biomedical engineering online · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Millions of breast imaging exams are performed each year in an effort to reduce the morbidity and mortality of breast cancer. Breast imaging exams are performed for cancer screening, diagnostic work-up of suspicious findings, evaluating extent of disease in recently diagnosed breast cancer patients, and determining treatment response. Yet, the interpretation of breast imaging can be subjective, tedious, time-consuming, and prone to human error. Retrospective and small reader studies suggest that deep learning (DL) has great potential to perform medical imaging tasks at or above human-level performance, and may be used to automate aspects of the breast cancer screening process, improve cancer detection rates, decrease unnecessary callbacks and biopsies, optimize patient risk assessment, and open up new possibilities for disease prognostication. Prospective trials are urgently needed to validate these proposed tools, paving the way for real-world clinical use. New regulatory frameworks must also be developed to address the unique ethical, medicolegal, and quality control issues that DL algorithms present. In this article, we review the basics of DL, describe recent DL breast imaging applications including cancer detection and risk prediction, and discuss the challenges and future directions of artificial intelligence-based systems in the field of breast cancer.
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.