Ultrasound-based Artificial Intelligence for Classification of Carpal Tunnel Syndrome

NCT06697392 | Active Not Recruiting DMC

• 1 other identifier: 2024PHB019-001(5)
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Carpal Tunnel Syndrome (CTS); Ultrasound; Artificial Intelligence (AI)

Keywords

carpal tunnel syndrome; ultrasound; artificial intelligence

Outcome Measures

Primary Outcomes (1)

• grading of CTS

  baseline

Study Arms (1)

Prospective test set

Other: ultrasound examination

Interventions

ultrasound examination OTHER

The investigators intend to perform ultrasound examinations for the participants with CTS.

Prospective test set

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Peking University People's Hospital: lead

Study Sites (1)

Peking University People's Hospital

Beijing, Beijing. PR, 100032, China

Location

Related Publications (3)

• 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.

  PMID: 32030660 BACKGROUND

• 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.

  PMID: 37082094 BACKGROUND

• 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.

  PMID: 36980446 BACKGROUND

MeSH Terms

Conditions

Carpal Tunnel Syndrome

Condition Hierarchy (Ancestors)

Median Neuropathy; Mononeuropathies; Peripheral Nervous System Diseases; Neuromuscular Diseases; Nervous System Diseases; Nerve Compression Syndromes; Cumulative Trauma Disorders; Sprains and Strains; Wounds and Injuries

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Doctor

Study Record Dates

• First Submitted: November 17, 2024
• First Posted: November 20, 2024
• Study Start: November 15, 2024
• Primary Completion: June 30, 2025
• Study Completion (Estimated): December 30, 2026
• Last Updated: November 20, 2024

Record last verified: 2024-11

Locations

CN (1)