Deep-learning For Ultrasound Classification of Anterior Talofibular Ligament Injury

NCT06372873 | Active Not Recruiting DMC

• 1 other identifier: 2023PHB211-001
• Study Type: observational
• Target: 3,000
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Deep Learning; Ultrasound; Anterior Talofibular Ligament

Outcome Measures

Primary Outcomes (1)

• 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

Study Arms (4)

Group I

mild-strain injury of ATFL

Other: re-evaluate by two senior radiologists in our medical center

Group II

partial ligament tears of ATFL

Other: re-evaluate by two senior radiologists in our medical center

Group III

complete rupture of ATFL

Other: re-evaluate by two senior radiologists in our medical center

Group IV

avulsed fractures

Other: re-evaluate by two senior radiologists in our medical center

Interventions

re-evaluate by two senior radiologists in our medical center OTHER

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

Group I; Group II; Group III; Group IV

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

As mentioned above

You may qualify if:

• 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.

You may not qualify if:

• 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)

Sponsors & Collaborators

• Peking University People's Hospital: lead

Study Sites (1)

Peking University People's Hospital

Beijing, Beijing Municipality, 100032, China

Location

Related Publications (4)

• 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.

  PMID: 27259753 BACKGROUND

• 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.

  PMID: 37510068 BACKGROUND

• 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.

  PMID: 35343253 BACKGROUND

• 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.

  PMID: 35216752 BACKGROUND

Study Officials

• Jiaan Zhu, Dr

  Peking University People's Hospital

  PRINCIPAL INVESTIGATOR

Study Design

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

Study Record Dates

• First Submitted: April 15, 2024
• First Posted: April 18, 2024
• Study Start: April 1, 2024
• Primary Completion: April 30, 2024
• Study Completion: May 30, 2025
• Last Updated: April 23, 2024

Record last verified: 2024-04

Locations

CN (1)