ReviewAbdominal radiology (New York)2018
Machine learning for medical ultrasound: status, methods, and future opportunities.
Review in Abdominal radiology (New York), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 95 papers, 1 of them a synthesis 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
95 citing papers in PubMed, 1 synthesis or guideline pooled it, 254 citations in OpenAlex.
- Ultrasound Radiomics for Differential Diagnosis of Renal Masses: A Systematic Review and Meta-Analysis.Technology in cancer research & treatmentPooled it
- Portable Ultrasound Bladder Volume Measurement Over Entire Volume Range Using a Deep Learning Artificial Intelligence Model in a Selected Cohort: A Proof of Principle Study.Neurourology and urodynamics · 2025Trial
- Optimizing Renal Fibrosis Assessment in Patients With Chronic Kidney Disease: Feature and Decision-Level Fusion in Machine Learning Using Clinical and Ultrasound Information.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026Article
- Diagnostic performance of artificial intelligence in fracture detection with ultrasound: A systematic review and meta-analysis.The ultrasound journal · 2026Article
- Deep-learning-Assisted Photoacoustic and Ultrasound Evaluation for Pre-transplant Human Liver Graft Quality and Transplant Suitability.medRxiv : the preprint server for health sciences · 2026Article
- Review
- Calcaneal spur detection from lateral foot radiographs using deep feature engineering.Scientific reports · 2026Article
- SpaceTime-SonoNet: efficient classification of ultra-sound video sequences.Medical & biological engineering & computing · 2026Article
- Advancements in artificial intelligence applications for liver ultrasound imaging.BJR artificial intelligence · 2026Review
- Ultrasound-based artificial intelligence for breast lesion classification.Frontiers in oncology · 2026Review
- A Wearable Ultrasound Sensing System for Soft Tissue Stiffness Detection: A Feasibility Study.Biosensors · 2025Article
- Artificial intelligence in contrast enhanced ultrasound: A new era for liver lesion assessment.World journal of gastroenterology · 2025Review
- The Use of Ultrasound Imaging in Continuous Blood Vessel Area and Velocity Data Acquisition for Determining the Local Pulse Wave Velocity.Journal of clinical medicine · 2025Review
- Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis.World journal of gastroenterology · 2025Review
- Article
- BCDCNN: breast cancer deep convolutional neural network for breast cancer detection using MRI images.Scientific reports · 2025Article
- AI-Assisted LVEF Assessment Using a Handheld Ultrasound Device: A Single-Center Comparative Study Against Cardiac Magnetic Resonance Imaging.Journal of clinical medicine · 2025Article
- Advancements in Medical Radiology Through Multimodal Machine Learning: A Comprehensive Overview.Bioengineering (Basel, Switzerland) · 2025Review
- Article
- Review
35 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization.
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.