We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




New Machine Learning Initiative Announced to Develop an Artificial Intelligence X-Ray Engine

By MedImaging International staff writers
Posted on 03 May 2016
Print article
An initiative has been set up between the largest US cloud-based radiology service provider, a leading US technology institute, and a leading medical school to develop an Artificial Intelligence (AI) X-Ray engine that can pre-read digital X-Ray exams and find potential injuries and disease.

The research team plans to integrate the system into the cloud-based service provider’s exam routing technology and apply an AI algorithm to all X-Ray images before they are routed to a radiologist. The system will use machine learning to continuously improve outcomes. The system will use the cloud provider’s imaging database of 7 billion images.

The Singularity Healthcare initiative will be launched in the second quarter of 2016 by Imaging Advantage (IA; Santa Monica, CA, USA), and includes leading researchers from the Massachusetts Institute of Technology (MIT; Boston, MA, USA) and Harvard Medical School/Massachusetts General Hospital (HMS/MGH; Boston, MA, USA). More than 500 radiologists and 450 facilities in the US and around the globe are connected to the Imaging Advantage cloud.

X-Ray exams still account for 50% of healthcare radiology tests in the US, and this makes them a significant limiting factor in hospital emergency departments. The Singularity initiative aims to make the patient workflow more efficient and improve treatment.

SP Kothari, PhD, MIT Sloan School of Management, said, "We have a number of opportunities for research and innovation at MIT, but were particularly intrigued by the bold initiative proposed by Imaging Advantage. Given IA's platform approach to healthcare delivery, national scale and significant imaging data set, and the contribution of Dr. Saini from MGH, one of the leading global radiology teaching and research institutions, the project is not only achievable, but also has potential to touch nearly every person in world. This is how we think artificial intelligence and deep learning should be developed and deployed. Given the advances in the field of artificial intelligence that have taken place at MIT and elsewhere, and Imaging Advantage's scale, we are not only optimistic about a successful outcome, but expect it to be realized on an accelerated schedule."

Related Links:
Imaging Advantage
Massachusetts Institute of Technology
Harvard Medical School

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
New
Ultrasound System
P20 Elite
DR Flat Panel Detector
1500L
New
Digital Radiography Generator
meX+20BT lite

Print article
Radcal

Channels

MRI

view channel
Image: PET/MRI can accurately classify prostate cancer patients (Photo courtesy of 123RF)

PET/MRI Improves Diagnostic Accuracy for Prostate Cancer Patients

The Prostate Imaging Reporting and Data System (PI-RADS) is a five-point scale to assess potential prostate cancer in MR images. PI-RADS category 3 which offers an unclear suggestion of clinically significant... Read more

Nuclear Medicine

view channel
Image: The new SPECT/CT technique demonstrated impressive biomarker identification (Journal of Nuclear Medicine: doi.org/10.2967/jnumed.123.267189)

New SPECT/CT Technique Could Change Imaging Practices and Increase Patient Access

The development of lead-212 (212Pb)-PSMA–based targeted alpha therapy (TAT) is garnering significant interest in treating patients with metastatic castration-resistant prostate cancer. The imaging of 212Pb,... Read more

General/Advanced Imaging

view channel
Image: The Tyche machine-learning model could help capture crucial information. (Photo courtesy of 123RF)

New AI Method Captures Uncertainty in Medical Images

In the field of biomedicine, segmentation is the process of annotating pixels from an important structure in medical images, such as organs or cells. Artificial Intelligence (AI) models are utilized to... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible

Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.