Artificial Intelligence Simplifies Cervical Cancer Screening
Wednesday, January 27, 2021
Even though cervical cancer is considered one of the most preventable forms of cancer, it remains a serious and deadly scourge for many across the world. A computer algorithm designed to quickly and easily identify pre-cancerous changes using a regular smartphone may change that.
“The point of everything that we do and have done in the last 40 years is to understand something deeply so that we can invent simple tools to use,” says IRP senior investigator, Mark Schiffman, M.D., M.P.H. To that end, he and collaborators in the National Cancer Institute (NCI) and the National Library of Medicine (NLM), in collaboration with the Global Health Labs and Unitaid, developed and are now testing a machine learning-based approach to screening for cervical cancer, with promising results.
Study Results Could Help Improve Treatment for Alcohol-Related Problems
Tuesday, April 16, 2019
Your brain is always busy, even when you’re not thinking about anything. Scientists believe the way brain cells communicate with one another when the brain is in that ‘resting state’ might differ in individuals with certain diseases. In a recent study of this idea, IRP researchers found that resting state brain activity could effectively predict the severity of alcohol-related problems.
Thursday, August 2, 2018
For most of their history, computers have been limited to mindlessly executing the instructions their programmers give them. However, recent advances have given rise to the intertwined fields of artificial intelligence (AI) and machine learning, which focus on the creation of computer programs that can operate independently and even teach themselves to perform specific, specialized tasks. In 2013, the online PubMed database listed only 200 research publications related to ‘deep learning,’ a new type of machine learning that has shown success for particularly difficult tasks like object and speech recognition. Just four years later, in 2017, that number exceeded 1,100.
Thursday, March 29, 2018
Alex Fuksenko, a senior at the University of Maryland in College Park, spent his summer in the lab of NIH IRP Investigator Kevin Briggman, Ph.D.
Fuksenko helped to create a website called Labrainth that “gamifies” the identification and tracing of neurons in 2D images produced by electron microscopes. By visiting the website and completing those activities, members of the public can earn points and move up leaderboards while producing data that machine learning algorithms can use to learn how to trace neurons in these images themselves, a necessary step towards producing an accurate 3D model of the human brain.