Clinical trial NCT06372873: Deep-learning For Ultrasound Classification of Anterior Talofibular Ligament Injury
Official title: Deep Learning-enabled Ultrasound Classification of Anterior Talofibular Ligament Injury in China: A Retrospective, Multicentre, Diagnostic Study
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Deep Learning
Ultrasound
Anterior Talofibular Ligament
Lead sponsor: Peking University People's Hospital
Sponsor class: OTHER
First posted: 2024-04-18
Enrollment: 3000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: As mentioned above

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.


Eligibility criteria: 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)

Primary outcomes:
measure: To evaluate whether the US images are in consensus with the ATFL injury classification of the reference standard
timeFrame: Baseline
Secondary outcomes:
Other outcomes:
Locations:
city: Beijing
country: China
facility: Peking University People's Hospital
state: Beijing
Study officials:
affiliation: Peking University People's Hospital
name: Jiaan Zhu, Dr
role: PRINCIPAL_INVESTIGATOR
References:
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.
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.
citation: 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.
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