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
当前地点:
>
> This Story


注册记数器 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?

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

THE (LEADER) IN MEDICAL IMAGING TECHNOLOGY SINCE 1982. SALES-SERVICE-REPAIR

Special-Pricing Available on Medical Displays, Patient Monitors, Recorders, Printers, Media, Ultrasound Machines, and Cameras.This includes Top Brands such as SONY, BARCO, NDS, NEC, LG, EDAN, EIZO, ELO, FSN, PANASONIC, MITSUBISHI, OLYMPUS, & WIDE.



"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

做广告
提升您的品牌知名度
拍卖+私人销售
获得最好的价格
买设备/配件
找到最低价格
每日新闻
阅读最新信息
目录
浏览所有的DOTmed用户
DOTmed上的伦理
查看我们的伦理计划
金子分开供营商节目
接收PH要求
金子服务经销商节目
接收请求
提供保健服务者
查看所有的HCP(简称医疗保健提供商)的工具
工作/训练
查找/申请工作
Parts Hunter +EasyPay
获取配件报价
最近证明
查看最近通过认证的用户
最近额定
查看最近通过认证的用户
出租中央
租用设备优惠
卖设备/配件
得到最划算
服务技术员论坛
查找帮助和建议
简单的征求建议书
获取设备报价
真正商业展览
查找对设备的服务
对这个站点的通入和用途是受期限和条件我们支配 法律公告 & 保密性通知
物产和业主对 DOTmed.com,公司 Copyright ©2001-2019 DOTmed.com, Inc.
版权所有