Ignite Creation Date:
2024-05-06 @ 12:48 PM
Last Modification Date:
2024-10-26 @ 1:04 PM
Study NCT ID:
NCT03851497
Status:
COMPLETED
Last Update Posted:
2021-03-26
First Post:
2019-02-21
Brief Title:
Application of Deep-learning and Ultrasound Elastography in Opportunistic Screening of Breast Cancer
Sponsor:
Peking Union Medical College Hospital
Organization:
Peking Union Medical College Hospital
Study Overview
Official Title:
A Multi-center Study of Deep Learning Diagnosis and Ultrasound Elastography in Opportunistic Screening of Breast Cancer
Status:
COMPLETED
Status Verified Date:
2021-03
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:
As the most common cancer expected to occur all over the world breast cancer still faces with the unsatisfied diagnostic accuracy in US imaging S-detect is a sophisticated CAD system for breast US imaging based on deep learning algorithms E-breast is a software installed in US machines which automatically reveals tumor elastographic features This multi-center study intends to further validate the diagnostic efficiency of S-detect and E-breast in opportunistic breast cancer screening populations in China Our hypothesis is that S-detect and E-breast can increase the diagnostic accuracy and specificity as compared to routinely US examinations by doctors
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