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
SEARCH
Current Location:
>
> This Story


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

 

 

MRI Homepage

MR could predict survival and treatment response for patients with brain metastasis May spare them life-threatening surgeries

New nanoparticle technology may replace MR for cancer detection Shows promise in preclinical study

Study finds bi-annual MR exams better option for young women with genetic risk Annual mammogram may not be necessary

Researchers develop MR technique to predict and prevent pregnancy complications Tracking water molecules to assess placenta health

Community practices not abiding by breast cancer screening guidelines for MR High breast density isn't enough

Mayo Clinic expert emphasizes safety demands of interventional MR procedures Ideal solution is a dedicated iMR site

Top 10 trends and takeaways from RSNA 2017 Are radiologists going the way of the dodo bird? Of course not / Yes, yes they are

Sectra announces MR and ultrasound monitoring capabilities for DoseTrack Will no longer be limited to information from ionizing sources

Expert makes the case for patient access to radiology reports Patients want a more active role in their care

GE debuts new flexible, blanket-like MR coils at RSNA Improves patient comfort during whole body exams

More research is needed for clinical use

Training computers to identify a depression candidate's brain MR scan

by Lauren Dubinsky , Senior Reporter
A combination of MR imaging and machine learning may be able to detect individuals who are susceptible to depression before its onset, according to a new study conducted by the University of Texas at Austin.

The researchers are using a supercomputer to train a machine learning algorithm to identify commonalities among hundreds of patients. Brain MR images, genomics data and other factors will help it to accurately predict those at risk of depression and anxiety.

Story Continues Below Advertisement

CT, MRI, NM, SPECT/CT, PET & PET/CT service, refurbished systems and parts

Accelerate your ROI with our Black Diamond Certified refurbished systems. One year warranty - ISO 13485 Certified - FDA registered - Over 65k parts in inventory



The type of machine learning that the team tested is called Support Vector Machine Learning. They provided a set of training examples that belonged to healthy individuals or those with depression.

The researchers labeled features in the data they considered to be meaningful, and then the examples were used to train the algorithm. A computer scanned the data to find subtle connections between disparate parts, and then constructed a model that assigns new examples to the healthy or depression category.

For the study, the team analyzed brain data from 52 individuals who were seeking treatment for depression and 45 healthy controls. They matched a subset of depressed participants with healthy individuals based on age and gender to compare the groups.

The participants underwent diffusion tensor imaging MR exams. From that data, the team compared fractional anisotropy measurements between the two groups and found statistically significant differences.

The study proved that DTI-derived fractional anisotropy maps can accurately identify those with or at risk of depression versus healthy controls.

The scale and complexity of the research made it impossible for the team to look at each scan, so machine learning was required to automate the discovery process.

"This is the wave of the future," David Schnyer, lead researcher, cognitive neuroscientist and professor at the university said in a statement. "We're seeing increasing numbers of articles and presentations at conference on the application of machine learning to solve difficult problems in neuroscience."

He cautions that even though the results are promising, they're not enough to be used as a clinical metric. But he believes that the algorithm can perform much better by adding more genomic data.

Schnyer and his team will expand their study to include data from several hundred volunteers from the Austin community who have been diagnosed with depression, anxiety or a related condition.

In addition, a new supercomputer will come online at the university later in the year that will be twice as powerful as the current system. That will provide better computer processing power, which is needed to incorporate more data and achieve greater accuracy.

MRI 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 ©2001-2017 DOTmed.com, Inc.
ALL RIGHTS RESERVED