ArticleMedical & biological engineering & computing2022
A deep learning approach to median nerve evaluation in ultrasound images of carpal tunnel inlet.
Article in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- The use of artificial intelligence in musculoskeletal ultrasound: a systematic review of the literature.La Radiologia medica · 2024Pooled it
- Artificial intelligence in ultrasound imaging for carpal tunnel syndrome: what clinicians need to know.Journal of medical ultrasonics (2001) · 2026Review
- Automated Segmentation of Diffuse and Multifocal Nerve Enlargement in Immune-Mediated Neuropathy Using Temporal Deep Learning on Continuous Ultrasound Scans.Diagnostics (Basel, Switzerland) · 2026Article
- Masked autoencoder pretraining for peripheral nerve segmentation in ultrasound images.Scientific reports · 2026Article
- Noninvasive Assessment of the Ulnar Nerve in the Upper Extremity Using Machine Learning.Journal of hand surgery global online · 2026Article
- Automated Segmentation of Median Nerve Cross-Sectional Area in Healthy Controls and Patients With Carpal Tunnel Syndrome Using a Convolutional Neural Network.Muscle & nerve · 2026Article
- Carpal Tunnel Syndrome Diagnosis: A Narrative Review of Complementary Roles of Neuromuscular Ultrasound and Electrodiagnostic Studies.Medical science monitor : international medical journal of experimental and clinical research · 2026Review
- A neuromuscular clinician's primer on machine learning.Journal of neuromuscular diseases · 2026Review
- Ultrasonographic Median Nerve Cross-Sectional Area and Clinical, Electrodiagnostic, and Laboratory Biomarkers in Electrodiagnostically Confirmed Carpal Tunnel Syndrome: A Single-Center Correlational Study.Diagnostics (Basel, Switzerland) · 2025Article
- Effectiveness of Comprehensive Video Datasets: Toward the Development of an Artificial Intelligence Model for Ultrasonography-Based Severity Diagnosis of Carpal Tunnel Syndrome.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2025Article
- Automated segmentation of the median nerve in patients with carpal tunnel syndrome.Scientific reports · 2024Article
- Convolutional Neural Network Approaches in Median Nerve Morphological Assessment from Ultrasound Images.Journal of imaging · 2024Article
- Is the diagnostic model based on convolutional neural network superior to pediatric radiologists in the ultrasonic diagnosis of biliary atresia?Frontiers in medicine · 2023Article
- From Visualization to Automation: A Narrative Review of Deep Learning's Impact on Ultrasound-based Median Nerve Assessment.Journal of medical ultrasoundReview
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ultrasound (US) imaging is recognized as a useful support for Carpal Tunnel Syndrome (CTS) assessment through the evaluation of median nerve morphology. However, US is still far to be systematically adopted to evaluate this common entrapment neuropathy, due to US intrinsic challenges, such as its operator dependency and the lack of standard protocols. To support sonographers, the present study proposes a fully-automatic deep learning approach to median nerve segmentation from US images. We collected and annotated a dataset of 246 images acquired in clinical practice involving 103 rheumatic patients, regardless of anatomical variants (bifid nerve, closed vessels). We developed a Mask R-CNN with two additional transposed layers at segmentation head to accurately segment the median nerve directly on transverse US images. We calculated the cross-sectional area (CSA) of the predicted median nerve. Proposed model achieved good performances both in median nerve detection and segmentation: Precision (Prec), Recall (Rec), Mean Average Precision (mAP) and Dice Similarity Coefficient (DSC) values are 0.916 ± 0.245, 0.938 ± 0.233, 0.936 ± 0.235 and 0.868 ± 0.201, respectively. The CSA values measured on true positive predictions were comparable with the sonographer manual measurements with a mean absolute error (MAE) of 0.918 mm
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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.