Clinical trial NCT06697392: Ultrasound-based Artificial Intelligence for Classification of Carpal Tunnel Syndrome
Official title: Ultrasound-based Artificial Intelligence for Grading of Carpal Tunnel Syndrome, a Multicenter Study in China
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Carpal Tunnel Syndrome (CTS)
Ultrasound
Artificial Intelligence (AI)
Keywords: carpal tunnel syndrome
ultrasound
artificial intelligence
Lead sponsor: Peking University People's Hospital
Sponsor class: OTHER
First posted: 2024-11-20
Enrollment: 500
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: PROSPECTIVE
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.

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.


Eligibility criteria: 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.

Primary outcomes:
measure: grading of CTS
timeFrame: baseline
Secondary outcomes:
Other outcomes:
Locations:
city: Beijing
country: China
facility: Peking University People's Hospital
state: Beijing. PR
Study officials:
References:
citation: 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.
citation: 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.
citation: 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.