ArticleUltrasonography (Seoul, Korea)2021
Technology trends and applications of deep learning in ultrasonography: image quality enhancement, diagnostic support, and improving workflow efficiency.
Article in Ultrasonography (Seoul, Korea), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- Diagnostic accuracy of B scan ultrasound for posterior segment ocular disorders: a meta-analysis.Documenta ophthalmologica. Advances in ophthalmology · 2025Pooled it
- The use of artificial intelligence in musculoskeletal ultrasound: a systematic review of the literature.La Radiologia medica · 2024Pooled it
- Artificial intelligence as medical device in radiology in 2025: the regulatory scenario in the EU, USA, and China.European radiology · 2026Review
- Identification of Hepatic Fibrosis and Steatosis via A Point-of-Care Transient Elastography System With Integrated AI.Liver international : official journal of the International Association for the Study of the Liver · 2026Article
- Impact of Simulated Artifacts on the Classification Performance of Apical Views in Transthoracic Echocardiography Using Convolutional Neural Networks.Bioengineering (Basel, Switzerland) · 2026Article
- Malignancy prediction for calcified thyroid nodules using deep learning based on ultrasound dynamic videos.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2025Review
- Comprehensive review of reinforcement learning for medical ultrasound imaging.Artificial intelligence review · 2025Article
- Review
- Deep learning prediction of renal anomalies for prenatal ultrasound diagnosis.Scientific reports · 2024Article
- A narrative review of deep learning in thyroid imaging: current progress and future prospects.Quantitative imaging in medicine and surgery · 2024Review
- Applications of artificial intelligence in musculoskeletal ultrasound: narrative review.Frontiers in medicine · 2023Review
- Convolutional neural network based on automatic segmentation of peritumoral shear-wave elastography images for predicting breast cancer.Frontiers in oncology · 2023Article
- Applications of artificial intelligence in obstetrics.Ultrasonography (Seoul, Korea) · 2023Article
- Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review.Diagnostics (Basel, Switzerland) · 2022Review
- Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study.Insights into imaging · 2022Article
- Basic principles and new advances in kidney imaging.Kidney international · 2021Review
- Artificial intelligence for ultrasonography: unique opportunities and challenges.Ultrasonography (Seoul, Korea) · 2021Article
- Automatic Liver Segmentation in CT Images with Enhanced GAN and Mask Region-Based CNN Architectures.BioMed research international · 2021Article
- Artificial intelligence: Revolutionizing pediatric emergency care - A narrative review.International journal of critical illness and injury scienceReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this review of the most recent applications of deep learning to ultrasound imaging, the architectures of deep learning networks are briefly explained for the medical imaging applications of classification, detection, segmentation, and generation. Ultrasonography applications for image processing and diagnosis are then reviewed and summarized, along with some representative imaging studies of the breast, thyroid, heart, kidney, liver, and fetal head. Efforts towards workflow enhancement are also reviewed, with an emphasis on view recognition, scanning guide, image quality assessment, and quantification and measurement. Finally some future prospects are presented regarding image quality enhancement, diagnostic support, and improvements in workflow efficiency, along with remarks on hurdles, benefits, and necessary collaborations.
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.