Ignite Creation Date:
2024-05-06 @ 8:25 PM
Last Modification Date:
2024-10-26 @ 3:27 PM
Study NCT ID:
NCT06372873
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-04-23
First Post:
2024-04-15
Brief Title:
Deep-learning For Ultrasound Classification of Anterior Talofibular Ligament Injury
Sponsor:
Peking University Peoples Hospital
Organization:
Peking University Peoples Hospital
Study Overview
Official Title:
Deep Learning-enabled Ultrasound Classification of Anterior Talofibular Ligament Injury in China A Retrospective Multicentre Diagnostic Study
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2024-04
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 ChinaThen the investigators divided the images into training dataset internal validation dataset and external validation dataset in a ratio of 811 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
The investigators collected US images of ATFL injuries which had arthroscopic surgery results as reference standard form 13 hospitals across ChinaThen the investigators divided the images into training dataset internal validation dataset and external validation dataset in a ratio of 811 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
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None