Clean Sweep Live Auction on Thur. March 28th. 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 Mobile Imaging
Current Location:
> This Story

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




MRI Homepage

BSWH to install Glassbeam's CLEAN blueprint to leverage machine uptime Will include integrated CMMS software by EQ2

NIH awards $1.8 million to Magnetic Insight for neurovascular MPI Detects magnetic nanoparticle tracers, enables deep-tissue imaging

Siemens focuses on digitalization at HIMSS Its expanded digital service portfolio will be on display

Prestige Medical Imaging partners with Esaote and Glassbeam Expands portfolio to include MR, ultrasound and analytical software

Fujifilm to unveil latest version of Synapse 3D platform at HIMSS Five new capabilities for advanced visualization

First 7T whole-body MR scanner in Canada installed in Montreal Produces high-resolution images at pixel dimensions measured in tenths of a millimeter

Medic Vision to deploy iQMR in China through new partnership with KAME Address extreme overload of imaging requests in China

Philips and MIM Software collab to streamline radiotherapy treatment planning Integrate portfolios of CT, MR and software solutions

Dennis Durmis MITA names chair of board of directors

Ohio State to treat epilepsy patients with focused ultrasound in world's first clinical trial For when seizures can't be controlled with medication

In a first, AI successfully predicts metastasis in MR breast images

by John W. Mitchell , Senior Correspondent
In another deep learning advance, researchers have been able to use a relatively small data set to teach an artificial intelligence application to deliver value-added imaging diagnostics. In a paper published in the Journal of Digital Imaging (JDI), the study team trained a program to predict a patient’s chances of developing breast cancer axillary lymph node metastases.

“Precision medicine [can] achieve the best outcome for patients with minimal harm,” Dr. Richard S. Ha, director of research and education, Breast Imaging Division, Columbia University Medical Center, told HCB News. “Conventional SLNB [biopsy], although effective, also has potential complications.”

Story Continues Below Advertisement


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.

The team compiled only 275 auxiliary lymph nodes for the study, split about evenly for positive and negative findings. Based on code that was developed in the study, a mean five-fold cross-validation prediction accuracy of 84.3 percent was realized. Ha said that recent advancements in GPU computing technology had enabled the development of artificial networks that can see patterns and features in digital images. This enables an AI application to classify images, which was applied in the study to sort metastatic and non-metastatic lymph nodes.

“The goal of the study is to utilize digital data in MR images to discern metastatic lymph nodes, with an ultimate future goal to minimize axillary surgeries in patients who may not benefit,” explained Ha.

He stressed that theirs was a feasibility study showing that it is possible to apply convolutional neural networks (CNN) to predict metastasis in lymph nodes. The team will next gather a larger AI training data set to improve their CNN algorithm further, to ultimately use it in a prospective clinical trial, according to Ha.

In the study discussion, the team noted that this was the first time that AI had been applied for predicting axillary lymph node metastasis. The study also noted that prior MR axilla studies alone were able to achieve 75 percent, compared to the 84 percent accuracy by the AI program.

“Applying deep machine learning using a CNN-based algorithm in our study, we were able to generate reasonable diagnostic performance in predicting axillary lymph node metastasis, even with a small data set,” concluded the authors. “Larger data set will likely improve our prediction model and can potentially be a non-invasive alternative to core needle biopsy and even sentinel lymph node evaluation.

The study was first posted online by JDI in April 2018 and published in the journal last month, December 2018.

Back to HCB News
  Pages: 1

MRI Homepage

You Must Be Logged In To Post A Comment

Increase Your
Brand Awareness
Auctions + Private Sales
Get The
Best Price
Buy Equipment/Parts
Find The
Lowest Price
Daily News
Read The
Latest News
Browse All
DOTmed Users
Ethics on DOTmed
View Our
Ethics Program
Gold Parts Vendor Program
Receive PH
Gold Service Dealer Program
Receive RFP/PS
Healthcare Providers
See all
HCP Tools
A Job
Parts Hunter +EasyPay
Get Parts
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
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.