ReviewJournal of medical ultrasound
From Visualization to Automation: A Narrative Review of Deep Learning's Impact on Ultrasound-based Median Nerve Assessment.
Review in Journal of medical ultrasound. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Automated segmentation and quantitative measurement of cervical nerves in ultrasound images using an SZJ-SEG-based deep learning framework.Quantitative imaging in medicine and surgery · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ultrasound imaging has emerged as a valuable tool in the diagnosis and management of peripheral nerve disorders. The integration of deep learning with ultrasound technology has shown remarkable potential in enhancing diagnostic accuracy and efficiency. This review focuses on the application of deep learning-based approaches in ultrasound diagnostics for peripheral nerve diseases, with particular emphasis on median nerve segmentation in carpal tunnel syndrome. Structured to provide a clear and thorough understanding of the current landscape and future directions, the paper is organized into several key sections: (1) an overview of the current state of the field, (2) research objectives and notable achievements, (3) challenges in dataset utilization specific to medical imaging, (4) trends in the development of deep learning models tailored for ultrasound analysis, and (5) potential future directions for research and clinical application. This review seeks to bridge the gap between technological advancements and clinical practice, potentially paving the way for more accurate and efficient diagnosis and treatment in the field of peripheral nerve disorders.
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.