artificial intelligence

Leveraging AI To Combat Cervical Cancer

IRP Researcher Identifies Precise Disease Biomarkers

doctor holding an electronic tablet

Over the last few decades, advances in cervical cancer screening and prevention have fundamentally changed the approach to dealing with one of the most common forms of cancer in younger women. While doctors have been able to detect cancerous and pre-cancerous cells with a Pap smear since the 1940s, the more recent discovery that the human papillomavirus (HPV) causes more than 90 percent of cervical cancers now affords greater accuracy to regular screening tests. What’s more, IRP researchers truly changed the game by developing a vaccine against HPV, which was approved by the U.S. Food and Drug Administration (FDA) in 2006. 

Still, despite these extremely positive developments, much work remains to be done, as cervical cancer continues to kill about 4,000 American women each year. In honor of World Cervical Cancer Awareness Month this January, we spoke with IRP Senior Investigator Nicolas Wentzensen, M.D., Ph.D., about his efforts to bring that number as close to zero as possible.

Say Hi to AI

NIH AI Symposium Highlights Potential of New Computational Tools

human head made out of computer circuits

The human brain is often compared to a computer. Although scientists and philosophers have long debated the appropriateness of that analogy, there’s no doubt that if our brains are computers, evolution takes its sweet time between software updates. Compare that to the rapid advancement of modern computers and it’s clear why many researchers are turning to software to assist the biological computer nature placed in their own heads.

On May 17, NIH celebrated this remarkable partnership between humans and machines with its first-ever Artificial Intelligence Symposium, a day-long event that brought together researchers from all around the IRP to share the ways their work is taking advantage of artificial intelligence (AI) and machine learning, which aims to create computers that can learn the way we do. Anyone in attendance surely came away in awe of the possibilities for how such technologies could accelerate our investigation into the mysteries of biology and the development of new medical treatments. For those who missed it, read on for a rundown of a few of the many research projects IRP researchers presented at the event.

Scouting Out Summer Poster Day

Annual Event Held In-Person for First Time in Four Years

Amia Black poses with her poster at Summer Poster Day

A few weeks ago, NIH’s Natcher Conference Center bustled with the youthful scientific enthusiasm of IRP summer interns for the first time since 2019, the last time that the IRP’s Summer Poster Day was held in-person. At this year’s event, held on August 3 and 4, the hundreds of high school and college students who conducted research in IRP labs as part of NIH’s Summer Internship Program this year eagerly showed off the fruits of their labors — from discoveries about how weight loss drugs affect the brain to new insights into a potential treatment target for age-related vision loss. Read on to learn more about a few of these scientific upstarts and the research revelations they’ve helped uncover.

IRP’s Bruce Tromberg Elected to National Academy of Medicine

Advances in Bioengineering Drive Life-Saving Medicine

Dr. Bruce Tromberg

“To discover new things, you need new ways to see them,” says Bruce J. Tromberg, Ph.D., Director of the National Institute of Biomedical Imaging and Bioengineering (NIBIB). That’s why he has spent the past 30 years of his career improving and inventing tools to help doctors and scientists conduct cutting-edge biomedical research and apply their findings to the task of saving lives. This past October, Dr. Tromberg was elected to the National Academy of Medicine (NAM) for his contributions to the fields of biophotonics and biomedical optics, as well as his leadership in the biomedical engineering and imaging community.

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.

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.

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.

Robot Superheroes to Big Data

As a child I liked robots. Growing up in Korea, I liked cartoons and movies where people were on a mission to save the world with the robots they invented, and I wanted to develop a superhero robot someday, too. While my robot isn’t yet complete, the path I followed in pursuit of my goals eventually led me to explore data analysis.

And here I am, a postdoc at the NIH—probably the largest healthcare research institution in the world—in the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory led by Dr. Ronald M. Summers. Our lab is part of the Department of Radiology and Imaging Sciences at the NIH Clinical Center.

Hoo-Chang Shin and Holger Roth