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
Current Location:
> This Story

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




Artificial Intelligence Homepage

Boston Children's Hospital teaming with GE Healthcare to develop radiology AI The first focus will reportedly be on brain scans

FDA clears Aidoc AI solution for flagging PE in chest CTs Speeding up the time between scan and diagnosis

Smart intelligence for trauma caregivers Insights from Pooja Rao, co-founder and R&D head for

AI tool matches radiologist in amyloid detection for Alzheimer's Processes entire whole-brain slice with 98.7 percent accuracy

New machine learning algorithm could decide who is best for heart failure treatment Could help prevent sudden death from heart failure

FDA clears Zebra Medical Vision's HealthPNX AI solution for pneumothorax Can detect 40 findings that indicate presence of condition

Lack of AI security puts IoT medical devices in danger of cyberattacks New report highlights evolving risks in healthcare

AI comparable to radiologists in prostate cancer detection accuracy Identifies and predicts aggressiveness using MR scans

FDA clears GE’s AI-based CT image reconstruction technology Available as upgrade to Revolution Apex scanner

Aidoc announces $27 million in VC funding to advance AI in imaging Brings company's total funding to $40 million

A new 'roadmap' has been developed
as a guide for research and
development of AI for medical

New 'roadmap' paves the way for AI innovations in radiology

by John R. Fischer , Staff Reporter
Medical imaging players now have a new ‘roadmap’ to go by for guidance in research and development of AI solutions to advance their field.

Developed from input collected at a workshop held by the National Institute of Health in August 2018, the report outlines key research themes and ways to advance foundational machine learning research for medical imaging.

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.

"The potential value of these machine learning methods to medical imaging is a recent discovery. The workshop and the publications themselves are strong evidence that the key stakeholders are working together to set the agenda," lead author Dr. Curtis P. Langlotz, a professor of radiology and biomedical informatics at Stanford University and RSNA Board liaison for information technology and annual meeting, told HCB News. "I expect they will continue to collaborate as the agenda is carried out."

While expected to advance clinical imaging practice in a number of fields — image reconstruction, noise reduction, segmentation, computer-aided detection and classification, and radiogenomics, among others — research on the use of AI is still in its early stages.

The aim behind the workshop was to instill collaboration and collect feedback on how to enhance opportunities and the pace of research around medical imaging AI, in order to address gaps in knowledge and prioritize areas of study. The result was the report, which points to specific innovations for producing more publicly available, validated and reusable data sets to serve as evaluation criteria for new algorithms and techniques.

Among the points it highlights are:

• new image reconstruction methods that efficiently produce images suitable for human interpretation from source data
• automated image labeling and annotation methods, including information extraction from the imaging report, electronic phenotyping, and prospective structured image reporting
• new machine learning methods for clinical imaging data, such as tailored, pre-trained model architectures, and distributed machine learning methods
• machine learning methods that can explain the advice they provide to human users (a.k.a. explainable artificial intelligence)
• validated methods for image de-identification and data sharing to facilitate wide availability of clinical imaging data sets.

It also specifies that useful data sets should rely on methods for rapidly creating labeled or annotated imaging data. In addition, novel pre-trained model architectures specifically for clinical imaging should be constructed with methods for distributed training that reduce the need for institutes to exchange data with one another.

The authors specify that fulfilling these objectives requires greater collaboration among standards bodies, professional societies, governmental agencies, private industry and other medical imaging stakeholders.

"RSNA published the results of its AI Summit recently, which is well aligned with this roadmap. I have no doubt the other co-sponsors of the NIH Worshop, the ACR and the Academy, will be pulling in the same direction," said Langlotz. "I am hopeful that we are on the road to a well-funded AI research ecosystem, both in foundational and in translational research. Today’s AI research will transform tomorrow’s medical imaging practice."

The workshop was cosponsored by the National Institute of Health, the Radiological Society of North America (RSNA), the American College of Radiology (ACR), and the Academy for Radiology and Biomedical Imaging Research.

The findings were published in the journal, Radiology.

Artificial Intelligence 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, Inc. Copyright ©2001-2019, Inc.