Deep-learning For Ultrasound Classification of Anterior Talofibular Ligament Injury

Ultrasound Clinical Trial

Official title:

Deep Learning-enabled Ultrasound Classification of Anterior Talofibular Ligament Injury in China: A Retrospective, Multicentre, Diagnostic Study

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT06372873
• Other study ID #: 2023PHB211-001
• Status: Active, not recruiting
• Start date: April 1, 2024
• Est. completion date: May 30, 2025

Study information

• Verified date: April 2024
• Source: Peking University People's Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Ultrasound (US) is a more cost-effective, accessible, and available imaging technique to assess anterior talofibular ligament (ATFL) injuries compared with magnetic resonance imaging (MRI). However, challenges in using this technique and increasing demand on qualified musculoskeletal (MSK) radiologists delay the diagnosis. Using datasets from multiple clinical centers, the investigators aimed to develop and validate a deep convolutional network (DCNN) model that automates classification of ATFL injuries using US images with the goal of providing interpretable assistance to radiologists and facilitating a more accurate diagnosis of ATFL injuries. The investigators collected US images of ATFL injuries which had arthroscopic surgery results as reference standard form 13 hospitals across China;Then the investigators divided the images into training dataset, internal validation dataset, and external validation dataset in a ratio of 8:1:1; the investigators chose an optimal DCNN model to test its diagnostic performance of the model, including the diagnostic accuracy, sensitivity, specificity, F1 score. At last, the investigators compared the diagnostic performance of the model with 12 radiologists at different levels of expertise.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 3000
• Est. completion date: May 30, 2025
• Est. primary completion date: April 30, 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 80 Years

Eligibility

Inclusion Criteria:

• age > 18 years old
• patients who had experienced an first-episode, acute ankle sprain and received US examination within 14 days post injury
• patients who had a corresponding arthroscopic surgery result for classification of the ATFL injury.

Exclusion Criteria:

• patients who had a previous history of ankle open trauma or ankle joint surgery
• there were any soft-tissue or bone tumors in the ankle
• there was concurrent with any other rheumatoid arthritis
• the image quality was low or there were severe artifacts (eg, anisotropic artifacts)

Related Conditions & MeSH terms

• Deep Learning
• Ultrasound

Intervention

Other:
• re-evaluate by two senior radiologists in our medical center
The allocated images obtained from the contributing hospitals will be re-evaluated by two senior radiologists in our clinical center

Locations

Country	Name	City	State
China	Peking University People's Hospital	Beijing	Beijing

Sponsors (1)

Lead Sponsor	Collaborator
Peking University People's Hospital

Country where clinical trial is conducted

China, 

References & Publications (4)

Cao M, Liu S, Zhang X, Ren M, Xiao Z, Chen J, Chen X. Imaging diagnosis for anterior talofibular ligament injury: a systemic review with meta-analysis. Acta Radiol. 2023 Feb;64(2):612-624. doi: 10.1177/02841851221080556. Epub 2022 Mar 27. — View Citation

Colo G, Bignotti B, Costa G, Signori A, Tagliafico AS. Ultrasound or MRI in the Evaluation of Anterior Talofibular Ligament (ATFL) Injuries: Systematic Review and Meta-Analysis. Diagnostics (Basel). 2023 Jul 10;13(14):2324. doi: 10.3390/diagnostics13142324. — View Citation

Gao Y, Zeng S, Xu X, Li H, Yao S, Song K, Li X, Chen L, Tang J, Xing H, Yu Z, Zhang Q, Zeng S, Yi C, Xie H, Xiong X, Cai G, Wang Z, Wu Y, Chi J, Jiao X, Qin Y, Mao X, Chen Y, Jin X, Mo Q, Chen P, Huang Y, Shi Y, Wang J, Zhou Y, Ding S, Zhu S, Liu X, Dong X, Cheng L, Zhu L, Cheng H, Cha L, Hao Y, Jin C, Zhang L, Zhou P, Sun M, Xu Q, Chen K, Gao Z, Zhang X, Ma Y, Liu Y, Xiao L, Xu L, Peng L, Hao Z, Yang M, Wang Y, Ou H, Jia Y, Tian L, Zhang W, Jin P, Tian X, Huang L, Wang Z, Liu J, Fang T, Yan D, Cao H, Ma J, Li X, Zheng X, Lou H, Song C, Li R, Wang S, Li W, Zheng X, Chen J, Li G, Chen R, Xu C, Yu R, Wang J, Xu S, Kong B, Xie X, Ma D, Gao Q. Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: a retrospective, multicentre, diagnostic study. Lancet Digit Health. 2022 Mar;4(3):e179-e187. doi: 10.1016/S2589-7500(21)00278-8. — View Citation

Gribble PA, Bleakley CM, Caulfield BM, Docherty CL, Fourchet F, Fong DT, Hertel J, Hiller CE, Kaminski TW, McKeon PO, Refshauge KM, Verhagen EA, Vicenzino BT, Wikstrom EA, Delahunt E. Evidence review for the 2016 International Ankle Consortium consensus statement on the prevalence, impact and long-term consequences of lateral ankle sprains. Br J Sports Med. 2016 Dec;50(24):1496-1505. doi: 10.1136/bjsports-2016-096189. Epub 2016 Jun 3. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	To evaluate whether the US images are in consensus with the ATFL injury classification of the reference standard	The radiologists in our clinical center will re-evaluate whether the US images are in consensus with the classification of ATFL injury of its reference standard	Baseline