ReviewDiagnostics (Basel, Switzerland)2020
The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review.
Review in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 2 of them syntheses that pooled it.
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
49 citing papers in PubMed, 2 syntheses or guidelines pooled it, 89 citations in OpenAlex.
- Artificial intelligence in breast ultrasound: a systematic review of research advances.Frontiers in oncology · 2025Pooled it
- Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review.Frontiers in oral health · 2021Pooled it
- Deep learning-based differentiation of FLASH ultrasound windows.BMC veterinary research · 2026Article
- A sample application designed for detection of teeth and jaw bone from cone-beam computed tomography images using deep learning methods.Oral radiology · 2026Article
- Integrating intratumoral and peritumoral ultrasound radiomics with clinicopathological features to predict axillary lymph node metastasis in early breast cancer.Gland surgery · 2026Article
- Breast cancer inter-image dissimilarity by feature optimization: An application of novel flea optimization algorithm.PloS one · 2026Article
- Evaluation of AI diagnostic systems for breast ultrasound: comparative analysis with radiologists and the effect of AI assistance.Japanese journal of radiology · 2025Article
- Correlation between ultrasonic features and expression of immunohistochemical factors in invasive ductal carcinoma of the breast.Quantitative imaging in medicine and surgery · 2025Article
- Variational mode directed deep learning framework for breast lesion classification using ultrasound imaging.Scientific reports · 2025Article
- Review
- Liver fibrosis stage classification in stacked microvascular images based on deep learning.BMC medical imaging · 2025Article
- Real-time AI-guided ultrasound localization method for breast tumor rotational resection.Frontiers in oncology · 2025Article
- Advancements in artificial intelligence for ultrasound diagnosis of ovarian cancer: a comprehensive review.Frontiers in oncology · 2025Review
- Diagnostic dilemma of lobular carcinoma: a mini-review of imaging modalities and the role of artificial intelligence and radiomics.Frontiers in oncology · 2025Review
- Artificial intelligence-based classification of breast nodules: a quantitative morphological analysis of ultrasound images.Quantitative imaging in medicine and surgery · 2024Article
- Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review.European radiology · 2024Article
- DAU-Net: Dual attention-aided U-Net for segmenting tumor in breast ultrasound images.PloS one · 2024Article
- Artificial Intelligence in Breast Imaging: Opportunities, Challenges, and Legal-Ethical Considerations.The Eurasian journal of medicine · 2023Article
- Automated breast tumor ultrasound image segmentation with hybrid UNet and classification using fine-tuned CNN model.Heliyon · 2023Article
- Examining the effectiveness of a deep learning-based computer-aided breast cancer detection system for breast ultrasound.Journal of medical ultrasonics (2001) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 1 institution in 1 country.
Funding
Abstract
Breast cancer is the most frequently diagnosed cancer in women; it poses a serious threat to women's health. Thus, early detection and proper treatment can improve patient prognosis. Breast ultrasound is one of the most commonly used modalities for diagnosing and detecting breast cancer in clinical practice. Deep learning technology has made significant progress in data extraction and analysis for medical images in recent years. Therefore, the use of deep learning for breast ultrasonic imaging in clinical practice is extremely important, as it saves time, reduces radiologist fatigue, and compensates for a lack of experience and skills in some cases. This review article discusses the basic technical knowledge and algorithms of deep learning for breast ultrasound and the application of deep learning technology in image classification, object detection, segmentation, and image synthesis. Finally, we discuss the current issues and future perspectives of deep learning technology in breast ultrasound.
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.