Deep-learning For Ultrasound Classification of Anterior Talofibular Ligament Injury

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

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Ultrasound; Deep Learning; Anterior Talofibular Ligament
• Intervention / Treatment: Other: re-evaluate by two senior radiologists in our medical center

• Study Type: Observational
• Enrollment (Estimated): 3000

Contacts and Locations

Study Locations

• China
  • Beijing
    • Beijing, Beijing, 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

As mentioned above

Description

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)

Study Plan

How is the study designed?

Number of groups / cohorts

4

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Group I; mild-strain injury of ATFL	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
Group II; partial ligament tears of ATFL	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
Group III; complete rupture of ATFL	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
Group IV; avulsed fractures	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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Peking University People's Hospital
• Investigators: Principal Investigator: Jiaan Zhu, Dr, Peking University People's Hospital

Publications and helpful links

General Publications

• 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.
• 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.
• 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.
• 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.

Study record dates

Study Major Dates

• Study Start (Actual): April 1, 2024
• Primary Completion (Estimated): April 30, 2024
• Study Completion (Estimated): May 30, 2025

Study Registration Dates

• First Submitted: April 15, 2024
• First Submitted That Met QC Criteria: April 15, 2024
• First Posted (Actual): April 18, 2024

Study Record Updates

• Last Update Posted (Actual): April 23, 2024
• Last Update Submitted That Met QC Criteria: April 19, 2024
• Last Verified: April 1, 2024

More Information

Terms related to this study

• Other Study ID Numbers: 2023PHB211-001

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