machine learning

Teaming Up to Tackle Engineering Challenges

Innovation Awards Accelerate Development of New Research Techniques

scientist working with a robotic arm

Scientists spend years, even decades, intensely studying a specific disease or biological system, an approach that yields unrivaled knowledge. However, many important scientific questions require a deep understanding of several subjects. As a result, the IRP has numerous programs dedicated to encouraging scientists with different areas of expertise to work together.

One such program is the NIH Director’s Challenge Innovation Awards, which funds innovative, high-impact projects that require the cooperation of researchers in more than one of NIH’s Institutes and Centers. This year, the program selected six promising proposals with one foot in the disciplines of biology and medicine and another in engineering or the physical sciences.

NIH Summer Interns Show Off in Poster Exhibitions

Budding Scientists Present Their Research During Three-Day Virtual Event

Deeya Garg

Although NIH’s 2021 Summer Internship Program (SIP) was fully virtual this year, that didn’t stop the hundreds of participating high school, college, and graduate students from contributing to a variety of important IRP research projects. More than 500 students who worked in NIH labs this summer presented their work during this year’s Summer Presentation Week, which took place August 3-5.

I sifted through the lengthy list of presenters at the event and spoke with a diverse group of young men and women who spent their summers expanding our knowledge of human health and biology. Read on to learn about these promising future scientists and doctors and the research they completed this summer.

AI Tools Provide Picture of Cervical Health

Artificial Intelligence Simplifies Cervical Cancer Screening

human silhouette containing computer circuits

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.

Brain Data Predicts Alcohol Disorder Symptoms

Study Results Could Help Improve Treatment for Alcohol-Related Problems

MRI images showing connectivity between different parts of the brain

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.

Doctor Data: How Computers Are Invading the Clinic

human silhouette surrounding a computer network

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.

Summertime Brains: Alex Fuksenko

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.