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
Current Location:
> This Story

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




More IT Matters

Improving efficiencies here and now with VR and 3D How one doctor with a passion for engineering aims to improve care

Pixel perfect – A new approach to annotation software Akshay Goel discusses cloud-based deep learning platform, Radlearn.ai

How AI can change radiology practice for the better A conversation with NYU Langone’s Dr. Michael Recht

Bringing a ‘hive mind’ approach to AI in radiology How Stanford Medical University and Unanimous AI are partnering to bring the human element to AI

IT Matters: Optimizing radiation therapy plans with AI Using automation to expedite cancer treatment planning

See All IT Matters  

Health IT Homepage

HHS releases second draft of TEFCA for nationwide interoperability Requirements for sharing electronic health information

Want to reduce readmissions? Let’s start with keeping patients healthier Insights from Robin Hill, chief clinical officer at Vivify Health

Decision support software could reduce scans by 6 percent: MIT researchers Prevent overuse of powerful and costly imaging exams

CMS to add more telehealth benefits to Medicare Advantage plans Aiming for greater flexibility, lower costs

Fredrik Palm ContextVision appoints new CEO

Trice Imaging connects imaging devices of large chain healthcare provider Aleris Patients and physicians can view images on laptops, cell phones

Three recommendations to better understand HIPAA compliance Approximately 70 percent of organizations are not HIPAA compliant

Researchers orchestrate malware attack to expose imaging vulnerabilities Deceived radiologists and AI algorithms into misdiagnoses

How hyper-targeting patient communications can improve medication adherence Providing specific messages can make a world of difference

Sound Imaging launches MR patient motion and detection system, SAMM MD Reduces repeat scans, prevents interruption to workflow

Dr. Luciano Prevedello

The promise of AI (part 2 of 2)

by Sean Ruck , Contributing Editor
From the September 2018 issue of DOTmed HealthCare Business News magazine

In last month’s HCB News, we spoke with Dr. Luciano Prevedello, a radiologist at the Ohio State University Wexner Medical Center and chief of the division of imaging informatics, about the past, present and future of artificial intelligence in healthcare, and the potential benefits (and challenges) it brings to medical imaging.In part two, Dr. Prevedello shares insight he’s gained through research and his own experience at the AI lab his radiology department created.

To start with, we talked about AI’s implementation. Prevedello believes that at least in the beginning, AI will need some degree of local development and/or validation prior to full implementation. New algorithms like deep learning have been performing extremely well, but tend to require large amounts of data for training and validation. Given that access to medical images is governed by several privacy rules, image sharing and algorithm development can only happen with the appropriate partnerships and agreements in place or at a site that has both access to the data and machine learning expertise. “Due to these limitations, researchers have been experimenting with different solutions. Instead of having data leave the institution to train algorithms at specialized facilities, one of the ideas is to have the algorithms come to the institutions. It is possible that this will become a trend in the future – the distribution of the platforms and algorithms to the institutions rather than data leaving the institution, but it will take time,” Prevedello said.

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 investment of time and money by AI pioneers looks like it will pay off, as even today, the technology is showing fairly good accuracy at classifying or identifying images of interest for radiologists. Prevedello cautioned, however, that it’s still far from perfect. He suggests that for now, one way to use these tools is to help improve workflow by pre-screening studies for potential critical findings thereby expediting diagnosis and treatment decisions. Prevedello warns against the indiscriminate use of these tools and believes that extensive validation is needed prior to implementation to assist with image interpretation. “First, we need to understand a lot more about the tools – when they fail and why. There’s still a lot of research that needs to happen,” he said.

Taking a look behind the scenes of what makes AI work gets technical, but it’s still digestible concepts at the surface level. Recent image classification tasks have been employing convolutional neural networks (CNN) to analyze visual information from either 2D or 3D datasets. One of the challenges is that there is extensive data variety and complexity in medical imaging. For example, while some modalities can produce color images, as one might see in some Doppler ultrasound exams, or in PET/CT, other technologies, like X-ray or MR generate grayscale images. These modalities also have different resolutions and different ways to represent specific body tissues. New algorithms need to account for these differences. “Many of the algorithms being used now were created based on a dataset called ImageNet,” said Prevedello. A challenge was created around ImageNet to create algorithms that could identify specific objects within images using AI. “ImageNet used photographic color images – an airplane, apple, oranges – the task was to classify images into one thousand object classes. The winning algorithms became very well-known and they’re used for multiple purposes today. In medical imaging, we use these algorithms as well. While they were tailored for color images, we learned how to reconfigure them for grayscale,” he said.
  Pages: 1 - 2 >>

Health IT 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.