Ultrasound-based Artificial Intelligence for Classification of Carpal Tunnel Syndrome

Ultrasound-based Artificial Intelligence for Grading of Carpal Tunnel Syndrome, a Multicenter Study in China

Carpal tunnel syndrome (CTS) is one of the most prevalent peripheral neuropathies, impacting approximately 4% of the general population. It is typically classified into three degrees: mild, moderate, and severe. Accurate grading of carpal tunnel syndrome (CTS) is essential for determining appropriate treatment options, thereby playing a crucial role in optimizing patient outcomes. Electrophysiological testing (EST) is a key parameter for grading carpal tunnel syndrome (CTS). However, it is limited by several factors, including its invasive nature, poor reproducibility, and reduced sensitivity for detecting early-stage disease. Recently, ultrasound has gained widespread acceptance among clinicians for the assessment and grading of CTS. Nonetheless, radiologists often encounter challenges in this process due to the variability in image quality, differences in experience, and inherent subjectivity.

To address these issues, artificial intelligence presents a promising solution. Therefore, this study aims to develop a deep learning model for grading CTS by leveraging multimodal imaging features, including B-mode ultrasound, superb microvascular imaging (SMI), and elastography. Additionally, the investigators intend to validate the model's effectiveness by testing it with images from various clinical centers, ensuring its generalizability across different clinical settings.

Study Overview

• Status: Active, not recruiting
• Conditions: Ultrasound; Carpal Tunnel Syndrome (CTS); Artificial Intelligence (AI)
• Intervention / Treatment: Other: ultrasound examination

• Study Type: Observational
• Enrollment (Estimated): 500

Contacts and Locations

Study Locations

• China
  • Beijing. PR
    • Beijing, Beijing. PR, China, 100032
      • Peking University People's Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

The investigators intend to perform ultrasound examinations for patients with idiopathic CTS adhering to specific inclusion and exclusion criteria, aiming to develop and test the efficacy of AI model for CTS grading.

Description

Inclusion Criteria:

• those who have complained about associated symptoms about CTS, including pain, numbness, and weakness of hand.
• those who perform ultrasound examinations of median nerve within 1 week of the symptom.
• those who have electrophysilogical test results as reference standard.

Exclusion Criteria:

• those who had a surgery in the affected hand.
• those who had a trauma or fracture in the affected hand.
• those who had rheumatoid-related conditions, autoimmune diseases, and endocrine disorders.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Prospective test set	Other: ultrasound examination; The investigators intend to perform ultrasound examinations for the participants with CTS.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Time Frame
grading of CTS	baseline

Collaborators and Investigators

• Sponsor: Peking University People's Hospital

Publications and helpful links

General Publications

• Lam KHS, Wu YT, Reeves KD, Galluccio F, Allam AE, Peng PWH. Ultrasound-Guided Interventions for Carpal Tunnel Syndrome: A Systematic Review and Meta-Analyses. Diagnostics (Basel). 2023 Mar 16;13(6):1138. doi: 10.3390/diagnostics13061138.
• Chan HP, Samala RK, Hadjiiski LM, Zhou C. Deep Learning in Medical Image Analysis. Adv Exp Med Biol. 2020;1213:3-21. doi: 10.1007/978-3-030-33128-3_1.
• Wielemborek PT, Kapica-Topczewska K, Pogorzelski R, Bartoszuk A, Kochanowicz J, Kulakowska A. Carpal tunnel syndrome conservative treatment: a literature review. Postep Psychiatr Neurol. 2022 Jun;31(2):85-94. doi: 10.5114/ppn.2022.116880. Epub 2022 May 31.

Study record dates

Study Major Dates

• Study Start (Actual): November 15, 2024
• Primary Completion (Estimated): June 30, 2025
• Study Completion (Estimated): December 30, 2026

Study Registration Dates

• First Submitted: November 17, 2024
• First Submitted That Met QC Criteria: November 17, 2024
• First Posted (Estimated): November 20, 2024

Study Record Updates

• Last Update Posted (Estimated): November 20, 2024
• Last Update Submitted That Met QC Criteria: November 17, 2024
• Last Verified: November 1, 2024

More Information

Terms related to this study

• Keywords: carpal tunnel syndrome; ultrasound; artificial intelligence
• Additional Relevant MeSH Terms: Nervous System Diseases; Wounds and Injuries; Pathologic Processes; Neuromuscular Diseases; Peripheral Nervous System Diseases; Disease; Median Neuropathy; Mononeuropathies; Nerve Compression Syndromes; Cumulative Trauma Disorders; Sprains and Strains; Syndrome; Carpal Tunnel Syndrome
• Other Study ID Numbers: 2024PHB019-001(5)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No