Liberty BioSecurity and Johns Hopkins University Release New Publication: Biofilms—Impacts on Human Health and Its Relevance to Space Travel
Liberty and NASA:JPL Release Research Publication on Assembled Genomes from Mars InSight Spacecraft Surfaces
Noninvasive test may screen for disease before symptoms appear
By Jim Dryden
It may be possible in the future to screen patients for Alzheimer’s disease using an eye exam.
Using technology similar to what is found in many eye doctors’ offices, researchers at Washington University School of Medicine in St. Louis have detected evidence suggesting Alzheimer’s in older patients who had no symptoms of the disease.
Their study, involving 30 patients, is published Aug. 23 in the journal JAMA Ophthalmology.
Authors: Paras Lakhani, MD, Adam B. Prater, MPH, MD, R. Kent Hutson, MD, Kathy P. Andriole, PhD, Keith J. Dreyer, DO, PhD, Jose Morey, MD, Luciano M. Prevedello, MD, MPH, Toshi J. Clark, MD, J. Raymond Geis, MD, Jason N. Itri, MD, PhD, C. Matthew Hawkins, MD
Much attention has been given to machine learning and its perceived impact in radiology, particularly in light of recent success with image classification in international competitions. However, machine learning is likely to impact radiology outside of image interpretation long before a fully functional “machine radiologist” is implemented in practice. Here, we describe an overview of machine learning, its application to radiology and other domains, and many cases of use that do not involve image interpretation. We hope that better understanding of these potential applications will help radiology practices prepare for the future and realize performance improvement and efficiency gains.