Deep-learning For Ultrasound Classification of Anterior Talofibular Ligament Injury
NCT ID: NCT06372873
Last Updated: 2024-04-23
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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ACTIVE_NOT_RECRUITING
3000 participants
OBSERVATIONAL
2024-04-01
2025-05-30
Brief Summary
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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.
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Detailed Description
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Conditions
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Study Design
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OTHER
RETROSPECTIVE
Study Groups
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Group I
mild-strain injury of ATFL
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
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
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
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
Interventions
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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
Eligibility Criteria
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Inclusion Criteria
* 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
* 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)
18 Years
80 Years
ALL
No
Sponsors
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Peking University People's Hospital
OTHER
Responsible Party
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Zhu Jiaan
Chairman
Principal Investigators
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Jiaan Zhu, Dr
Role: PRINCIPAL_INVESTIGATOR
Peking University People's Hospital
Locations
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Peking University People's Hospital
Beijing, Beijing Municipality, China
Countries
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References
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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.
Other Identifiers
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2023PHB211-001
Identifier Type: -
Identifier Source: org_study_id
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