dismiss

Clean Sweep Live Auction on Wed. May 1st. Click to view the full inventory

DOTmed Home MRI Oncology Ultrasound Molecular Imaging X-Ray Cardiology Health IT Business Affairs
News Home Parts & Service Operating Room CT Women's Health Proton Therapy Endoscopy HTMs Pediatrics
SEARCH
Current Location:
>
> This Story


Log in or Register to rate this News Story
Forward Printable StoryPrint Comment
advertisement

 

advertisement

 

Women's Health Homepage

Latest ACP mammo guidelines elicit strong opposition Experts say findings could lead to 10,000 more breast cancer deaths annually

Study supports 3D mammography for older women, contrary to USPTSF recommendation New data sheds light on risk-benefit ratio of screening older patients

Volpara and GE expand breast density software partnership GE will become global distributor of VolparaDensity software

FDA proposes changes to mammography regulations First agency efforts to 'modernize' breast screening in over two decades

Not all breast density laws are created equally Research shows that the wording of some notifications result in supplemental testing, others don't

3D mammography helps avoid unnecessary breast biopsies, says study 33 percent difference in biopsy rate compared to standard mammography

New study finds AI breast screening interpretations on par with those of radiologists Could relieve high labor intensity of screening programs

South Dakota passes breast density law Will require all women who undergo mammograms to be notified of their breast density status

FDA warns against thermography alone for breast cancer detection Not a substitute for mammography

Mammography reports nationwide to include patient breast density Federal law takes aim at ensuring breast density awareness

A new algorithm may be just as good
as an experienced mammographer
in interpreting breast density
says a study

Is AI a match for manual interpretation of breast density?

by John R. Fischer , Staff Reporter
A new algorithm designed to measure breast density may be just as accurate as an experienced mammographer, says a new study.

Breast imagers and AI experts at Massachusetts General Hospital (MGH) and Massachusetts Institute of Technology (MIT) have devised a new approach for automatically measuring breast density in an attempt to overcome the subjective discrepancies found in manual interpretations by different clinicians, and are using it at MGH in what marks the first example of a deep-learning mechanism of its kind to be implemented in clinical practice on real patients.

Story Continues Below Advertisement

RaySafe helps you avoid unnecessary radiation

RaySafe solutions are designed to minimize the need for user interaction, bringing unprecedented simplicity & usability to the X-ray room. We're committed to establishing a radiation safety culture wherever technicians & medical staff encounter radiation.



"Unfortunately, it is widely documented that radiologists' assessments of density are often inconsistent and highly subjective. Using machine computed density eliminates this inconsistency," Regina Barzilay, Delta Electronics professor of the Electrical Engineering and Computer Science Department at MIT, told HCB News.

The presence of dense breast tissue can mask tumors, preventing mammograms from detecting them and raising the risk of false negatives. Supplemental screening options, such as breast MR and ultrasound, though effective, may not be reimbursable and require expensive, out-of-pocket costs for patients.

Utilizing tens of thousands of high-quality, digital mammograms from MGH, researchers trained and tested the algorithm prior to implementing it in routine clinical practice. Eight radiologists then reviewed 10,763 findings determined by the model to be either dense or non-dense tissue, agreeing with its distinctions for 10,149 mammograms, the amount of which made up 94 percent of its total assessments.

Rejection of the other six percent, however, does not necessarily mean the algorithm was wrong when taking into consideration reader variability among radiologists. Barzilay says the next step is to develop technology that can predict future risks from images and combine those findings with those on breast density.

"While density correlates with risk, it doesn't on its own determine who is gonna get breast cancer," she said. "We are currently working on the algorithms that can predict future risk directly from images."

The researchers attribute the availability of high-quality, annotated data evaluations by radiologists and the collaborative efforts of experienced medical and computer science professionals as the key to the model’s success in clinical practice.

Approximately 16,000 images have been processed by the system since its implementation in January.

The study was published this month in the journal, Radiology.

Women's Health Homepage


You Must Be Logged In To Post A Comment

Advertise
Increase Your
Brand Awareness
Auctions + Private Sales
Get The
Best Price
Buy Equipment/Parts
Find The
Lowest Price
Daily News
Read The
Latest News
Directory
Browse All
DOTmed Users
Ethics on DOTmed
View Our
Ethics Program
Gold Parts Vendor Program
Receive PH
Requests
Gold Service Dealer Program
Receive RFP/PS
Requests
Healthcare Providers
See all
HCP Tools
Jobs/Training
Find/Fill
A Job
Parts Hunter +EasyPay
Get Parts
Quotes
Recently Certified
View Recently
Certified Users
Recently Rated
View Recently
Certified Users
Rental Central
Rent Equipment
For Less
Sell Equipment/Parts
Get The
Most Money
Service Technicians Forum
Find Help
And Advice
Simple RFP
Get Equipment
Quotes
Virtual Trade Show
Find Service
For Equipment
Access and use of this site is subject to the terms and conditions of our LEGAL NOTICE & PRIVACY NOTICE
Property of and Proprietary to DOTmed.com, Inc. Copyright ©2001-2019 DOTmed.com, Inc.
ALL RIGHTS RESERVED