Ignite Creation Date:
2024-05-06 @ 1:53 PM
Last Modification Date:
2024-10-26 @ 1:21 PM
Study NCT ID:
NCT04156880
Status:
WITHDRAWN
Last Update Posted:
2024-02-07
First Post:
2019-11-06
Brief Title:
Artificial Intelligence in Mammography-Based Breast Cancer Screening
Sponsor:
Chinese University of Hong Kong
Organization:
Chinese University of Hong Kong
Study Overview
Official Title:
Breast Cancer Screening With Mammography Diagnostic Assessment of an Artificial
Status:
WITHDRAWN
Status Verified Date:
2024-02
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
the collaborating party of AI system withdraw their study due to change of company policy in year of COVID-19 pandemic
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Breast cancer BC is the most common cancer among women in worldwide and the second leading cause of cancer-related death
As the corner stone of BC screening mammography is recognized as one of useful imaging modalities to reduce BC mortality by virtue of early detection of BC However mammography interpretation is inherently subjective assessment and prone to overdiagnosis
In recent years artificial intelligence AI-Computer Aided Diagnosis CAD systems characterized by embedded deep-learning algorithms have entered into the field of BC screening as an aid for radiologist with purpose to optimize conventional CAD system with weakness of hand-crafted features extraction For now stand-alone performance of novel AI-CAD tools have demonstrated promising accuracy and efficiency in BC diagnosis largely attributed to utilization of convolution neural networkCNNs and some of them have already achieved radiologist-like level On the other hand radiologists performance on BC screening has shown to be enhanced by leveraging AI-CAD system as decision support tool As increasing implementation of commercial AI-CAD system robust evaluation of its usefulness and cost-effectiveness in clinical circumstances should be undertaken in scenarios mimicking real life before broad adoption like other emerging and promising technologies This requires to validate AI-CAD systems in BC screening on multiple diverse and representative datasets and also to estimate the interface between reader and system This proposed study seeks to investigate the breast cancer diagnostic performance of AI-CAD system used for reading mammograms In this work we will employ a commercially available AI-CAD tool based on deep-learning algorithms IBM Watson Imaging AI Solution to identify and characterize the suspicious breast lesions on mammograms The potential cancer lesions can be labeled and their mammographic features and malignancy probability will be automatically reported After AI post-processing we shall further carry out statistical analysis to determine the accuracy of AI-CAD system for BC risk prediction
As the corner stone of BC screening mammography is recognized as one of useful imaging modalities to reduce BC mortality by virtue of early detection of BC However mammography interpretation is inherently subjective assessment and prone to overdiagnosis
In recent years artificial intelligence AI-Computer Aided Diagnosis CAD systems characterized by embedded deep-learning algorithms have entered into the field of BC screening as an aid for radiologist with purpose to optimize conventional CAD system with weakness of hand-crafted features extraction For now stand-alone performance of novel AI-CAD tools have demonstrated promising accuracy and efficiency in BC diagnosis largely attributed to utilization of convolution neural networkCNNs and some of them have already achieved radiologist-like level On the other hand radiologists performance on BC screening has shown to be enhanced by leveraging AI-CAD system as decision support tool As increasing implementation of commercial AI-CAD system robust evaluation of its usefulness and cost-effectiveness in clinical circumstances should be undertaken in scenarios mimicking real life before broad adoption like other emerging and promising technologies This requires to validate AI-CAD systems in BC screening on multiple diverse and representative datasets and also to estimate the interface between reader and system This proposed study seeks to investigate the breast cancer diagnostic performance of AI-CAD system used for reading mammograms In this work we will employ a commercially available AI-CAD tool based on deep-learning algorithms IBM Watson Imaging AI Solution to identify and characterize the suspicious breast lesions on mammograms The potential cancer lesions can be labeled and their mammographic features and malignancy probability will be automatically reported After AI post-processing we shall further carry out statistical analysis to determine the accuracy of AI-CAD system for BC risk prediction
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