English
Chinese (Simplified)
French
Portuguese
Spanish
German
Forward
Print
Comment
Health IT Homepage
Sectra showcases work-in-progress, AI functionality at RSNA Gives radiologist greater control over findings
AMA survey: Radiologists top users of telemedicine for patient interactions First nationally representative estimates on usage
Ramsoft partners with QliqSOFT Enables secure messaging of information on mobiles among doctors and patients
Agfa showcases rad/fluoro DR 800 system at RSNA Enhance patient and operator comfort
Amazon Comprehend Medical to bring natural language processing to healthcare A new HIPAA-eligible machine learning service
EBM Technologies brings PACS image viewing to iPad Pro at RSNA System works with any PACS, enabling FDA-cleared remote reading
Arterys touts cloud-native platform and regulatory approval in 98 countries AI capabilities with 'unmatched' security
Carestream unveils interactive multimedia and workflow enhancements Enhancing collaboration and productivity among providers and patients
Nuance launches AI Marketplace expansion with 40+ AI radiology 'app developer' network Also unveils cloud-based version of PowerScribe One reporting platform
University of Utah Health among first to leverage Philips Illumeo PACS with adaptive intelligence On display at RSNA
Machine learning algorithm may predict early-stage Alzheimer's disease
There is currently no cure for the disease, but several drugs have been shown to delay or prevent symptoms from worsening for up to five years or more, according to the National Institute on Aging and published research.
RamSoft PowerServer™ RIS/PACS - Enabling Efficient Diagnostic Imaging
RamSoft's PowerServer™ RIS/PACS is an intuitive, single database application that enables healthcare practices to operate diagnostic imaging more efficiently than ever before.Why is this important? Click to find out.
The program integrates different indicators of Alzheimer’s, such as mild cognitive impairment, that can be a precursor for the disease. In two consecutive stages, the program’s algorithm chooses the most relevant indicator to predict who has Alzheimer’s.
The Cascaded Multi-view Canonical Correlation (CaMCCo) algorithm integrates measurements from MR scans, features of the hippocampus, glucose metabolism rates in the brain, proteomics, genomics, MCI and other parameters.
The university has proved that integrating unrelated information is useful for identifying cancers, but this is the first time they used the same approach for diagnosing and characterizing Alzheimer’s.
Machine learning algorithms are being used to detect other conditions as well. In May, the University of Pennsylvania found that machine learning could help with the early detection of severe sepsis and Zebra Medical Vision developed an algorithm last year that can predict cardiovascular events.
The program then evaluated the variables in a two-stage process. It selects the parameters that best differentiate a healthy patient from one who isn’t, and then selects the unhealthy variables that best distinguish who has MCI and who has Alzheimer’s.
The team tested it using data from 149 patients that were obtained through the Alzheimer’s Disease Neuroimaging Initiative. For predicting which patients had Alzheimer’s, CaMCCo outperformed individual indicators as well as methods that combine them without selective assessment.
They are continuing to improve and validate the approach using data from multiple sites. They also have plans to use it in an observational mode in which the computer runs the data while a neurologist compiles tests on patients.
If the program proves to be useful for predicting early-stage Alzheimer’s, the team hopes to conduct a clinical trial for prospective validation.
You Must Be Logged In To Post A CommentRegisterRegistration is Free and Easy. Enjoy the benefits of The World's Leading New & Used Medical Equipment Marketplace. Register Now! |
|
