ArticleUltrasonography (Seoul, Korea)2021
Artificial intelligence in musculoskeletal ultrasound imaging.
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 28 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
28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 67 citations in OpenAlex.
- The use of artificial intelligence in musculoskeletal ultrasound: a systematic review of the literature.La Radiologia medica · 2024Pooled it
- Incidence, Prevalence, Risk Factors, and Clinical Treatment for Children with Developmental Dysplasia of the Hip in Saudi Arabia. A Systematic Review.Journal of epidemiology and global health · 2024Pooled it
- Masked autoencoder pretraining for peripheral nerve segmentation in ultrasound images.Scientific reports · 2026Article
- Progress in machine learning-assisted medical imaging for osteoarthritis and osteoporosis diagnosis: a narrative review.Quantitative imaging in medicine and surgery · 2026Review
- Echo Intensity Correction Method for Ultrasound Computed Tomography in Musculoskeletal Imaging.Bioengineering (Basel, Switzerland) · 2026Article
- Automated segmentation and classification of lumbar transverse ultrasound views using a two-stage deep learning method.Frontiers in radiology · 2026Article
- Applications of musculoskeletal ultrasound in inflammatory arthritis.Clinical medicine (London, England) · 2025Review
- Sarcopenia: Current Insights into Molecular Mechanisms, Diagnostics, and Emerging Interventional Approaches.International journal of molecular sciences · 2025Review
- Review
- Integration of Artificial Intelligence for Diagnostic Methods in Musculoskeletal Conditions: A Systematic Review.Cureus · 2025Review
- Evaluating the impact of sex bias on AI models in musculoskeletal ultrasound of joint recess distension.PloS one · 2025Article
- Practical Use of Ultrasound in Modern Rheumatology-From A to Z.Life (Basel, Switzerland) · 2024Review
- Deep Learning Detection and Segmentation of Facet Joints in Ultrasound Images Based on Convolutional Neural Networks and Enhanced Data Annotation.Diagnostics (Basel, Switzerland) · 2024Article
- Development of Continuous Assessment of Muscle Quality and Frailty in Older Patients Using Multiparametric Combinations of Ultrasound and Blood Biomarkers: Protocol for the ECOFRAIL Study.JMIR research protocols · 2024Article
- AI applications in musculoskeletal imaging: a narrative review.European radiology experimental · 2024Review
- Current status of dynamic musculoskeletal ultrasound for application to treatment of orthopedic diseases.American journal of translational research · 2024Review
- What is new in imaging to assist in the diagnosis of giant cell arteritis and Takayasu's arteritis since the EULAR and ACR/VF recommendations?Frontiers in medicine · 2024Review
- Identification of tophi in ultrasound imaging based on transfer learning and clinical practice.Scientific reports · 2023Article
- Neuromuscular Ultrasound in Intensive Care Unit-Acquired Weakness: Current State and Future Directions.Medicina (Kaunas, Lithuania) · 2023Review
- Applications of artificial intelligence in musculoskeletal ultrasound: narrative review.Frontiers in medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Ultrasonography (US) is noninvasive and offers real-time, low-cost, and portable imaging that facilitates the rapid and dynamic assessment of musculoskeletal components. Significant technological improvements have contributed to the increasing adoption of US for musculoskeletal assessments, as artificial intelligence (AI)-based computer-aided detection and computer-aided diagnosis are being utilized to improve the quality, efficiency, and cost of US imaging. This review provides an overview of classical machine learning techniques and modern deep learning approaches for musculoskeletal US, with a focus on the key categories of detection and diagnosis of musculoskeletal disorders, predictive analysis with classification and regression, and automated image segmentation. Moreover, we outline challenges and a range of opportunities for AI in musculoskeletal US practice.
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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.