Deep LearningNCT06372873
Deep-learning For Ultrasound Classification of Anterior Talofibular Ligament Injury
An observational study of Deep Learning and Ultrasound, run by Peking University People's Hospital at 1 site in China.
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). Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Deep Learning, Ultrasound, Anterior Talofibular Ligament
- What is being tested?
- re-evaluate by two senior radiologists in our medical center
- How many people
- 3,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Apr 2024to 30 May 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: As mentioned above
Who may be able to join
- 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.
Who cannot join
- 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)
Trial locations and contacts
1 site in 1 country.
China1 site
Peking University People's Hospital
Beijing, Beijing Municipality, 100032
About this study
Official title: 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.
- Conditions
- Deep Learning, Ultrasound, Anterior Talofibular Ligament
- Being tested
- re-evaluate by two senior radiologists in our medical center
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
re-evaluate by two senior radiologists in our medical centerOther
The allocated images obtained from the contributing hospitals will be re-evaluated by two senior radiologists in our clinical center
| Study group | Role | What they receive |
|---|---|---|
| Group I | mild-strain injury of ATFL Receives: Other: re-evaluate by two senior radiologists in our medical center | |
| Group II | partial ligament tears of ATFL Receives: Other: re-evaluate by two senior radiologists in our medical center | |
| Group III | complete rupture of ATFL Receives: Other: re-evaluate by two senior radiologists in our medical center | |
| Group IV | avulsed fractures Receives: Other: re-evaluate by two senior radiologists in our medical center |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Apr 2024
- Study start1 Apr 2024
- Primary completion30 Apr 2024 (estimated)
- Study completion30 May 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Deep LearningStudies of the same condition, open and under way.
- More trials run by Peking University People's HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 3,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University People's Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Zhu Jiaan, Peking University People's Hospital
- Investigators
- Jiaan Zhu, Dr, Principal investigator, Peking University People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06372873
- Sponsor's protocol ID
- 2023PHB211-001
- Status last verified
- Apr 2024
- First submitted
- 15 Apr 2024
- Last updated
- 23 Apr 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
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.
PubMed 27259753Colo 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.
PubMed 37510068Cao 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.
PubMed 35343253Gao 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.
PubMed 35216752
Study at a glance
From the registry record, last updated by the sponsor on 23 Apr 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning-enabled Ultrasound Classification of Anterior Talofibular Ligament Injury in China: A Retrospective, Multicentre, Diagnostic Study
- ClinicalTrials.gov ID
- NCT06372873
- Acronym / sponsor ID
- 2023PHB211-001
- Conditions
- Deep Learning, Ultrasound, Anterior Talofibular Ligament
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 3,000 participants (estimated)
- Lead sponsor
- Peking University People's HospitalUniversity, hospital or other
- Study start
- 1 Apr 2024 (actual)
- Primary completion
- 30 Apr 2024 (estimated)
- Study completion
- 30 May 2025 (estimated)
- First posted
- 18 Apr 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Group I | – | Other: re-evaluate by two senior radiologists in our medical centermild-strain injury of ATFL |
| Group II | – | Other: re-evaluate by two senior radiologists in our medical centerpartial ligament tears of ATFL |
| Group III | – | Other: re-evaluate by two senior radiologists in our medical centercomplete rupture of ATFL |
| Group IV | – | Other: re-evaluate by two senior radiologists in our medical centeravulsed fractures |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- As mentioned above
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (4 listed)
- 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)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| re-evaluate by two senior radiologists in our medical centerThe allocated images obtained from the contributing hospitals will be re-evaluated by two senior radiologists in our clinical center | Other | Group I, Group II, Group III, Group IV |
Primary outcome
To evaluate whether the US images are in consensus with the ATFL injury classification of the reference standard
Time frame: Baseline
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
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Apr 2024.
- Background: PubMed 27259753Gribble 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.
- Background: PubMed 37510068Colo 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.
- Background: PubMed 35343253Cao 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.
- Background: PubMed 35216752Gao 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.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Jiaan Zhu, Dr principal investigator
Peking University People's Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Deep Learning and Ultrasound, run by Peking University People's Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 23 Apr 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
To evaluate whether the US images are in consensus with the ATFL injury classification of the reference standard (time frame: Baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06372873, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 Apr 2024.