ArticleDiagnostics (Basel, Switzerland)2022
Diagnosis of Cubital Tunnel Syndrome Using Deep Learning on Ultrasonographic Images.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06090877 (Role of Ultrasound and Shear Wave Elastography in Diagnosis and Grading of Cubital Tunnel Syndrome), which is not on this map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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.
Role of Ultrasound and Shear Wave Elastography in Diagnosis and Grading of Cubital Tunnel Syndrome
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- The implementation of artificial intelligence in upper extremity surgery: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Surgical outcomes of cubital tunnel syndrome with acute symptom exacerbation caused by elbow ganglion cysts: a retrospective case series of 32 patients.BMC surgery · 2026Article
- Masked autoencoder pretraining for peripheral nerve segmentation in ultrasound images.Scientific reports · 2026Article
- An AI-Driven Pipeline for Localization, Segmentation, and Classification of Carpal Tunnel Syndrome Using Ultrasound Images of the Median Nerve.Hand (New York, N.Y.) · 2026Article
- A comprehensive review of ultrasonographic imaging in athletic elbow injuries: a compartment-based approach.Skeletal radiology · 2026Review
- Noninvasive Assessment of the Ulnar Nerve in the Upper Extremity Using Machine Learning.Journal of hand surgery global online · 2026Article
- Emergent technologies in clinical neurophysiology to study the neuromuscular system: IFCN handbook chapter.Clinical neurophysiology practice · 2026Review
- Using deep learning for ultrasound images to diagnose chronic lateral ankle instability with high accuracy.Asia-Pacific journal of sports medicine, arthroscopy, rehabilitation and technology · 2025Article
- Quantitative Evaluation of Tendon Gliding Sounds and Their Classification Using Deep Learning Models.Cureus · 2025Article
- Cubital tunnel syndrome: anatomy, pathology, and imaging.Skeletal radiology · 2025Review
- Advances in Ultrasound-Guided Surgery and Artificial Intelligence Applications in Musculoskeletal Diseases.Diagnostics (Basel, Switzerland) · 2024Review
- Role of high-resolution ultrasound and magnetic resonance neurography in the evaluation of peripheral nerves in the upper extremity.Journal of ultrasonography · 2023Article
- Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques.Diagnostics (Basel, Switzerland) · 2023Article
- Ulnar nerve thickness at the elbow on longitudinal ultrasound view in control subjects.Neurological research and practice · 2023Article
Corrections and comments
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Authors and funding
12 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although electromyography is the routine diagnostic method for cubital tunnel syndrome (CuTS), imaging diagnosis by measuring cross-sectional area (CSA) with ultrasonography (US) has also been attempted in recent years. In this study, deep learning (DL), an artificial intelligence (AI) method, was used on US images, and its diagnostic performance for detecting CuTS was investigated. Elbow images of 30 healthy volunteers and 30 patients diagnosed with CuTS were used. Three thousand US images were prepared per each group to visualize the short axis of the ulnar nerve. Transfer learning was performed on 5000 randomly selected training images using three pre-trained models, and the remaining images were used for testing. The model was evaluated by analyzing a confusion matrix and the area under the receiver operating characteristic curve. Occlusion sensitivity and locally interpretable model-agnostic explanations were used to visualize the features deemed important by the AI. The highest score had an accuracy of 0.90, a precision of 0.86, a recall of 1.00, and an F-measure of 0.92. Visualization results show that the DL models focused on the epineurium of the ulnar nerve and the surrounding soft tissue. The proposed technique enables the accurate prediction of CuTS without the need to measure CSA.
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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.