It’s time for another round of introductions! Many of you may already know Sameer Antani, PhD—one of NLM’s most decorated and prestigious investigators—from his many awards and accolades. In March 2022, he was inducted into the American Institute for Medical and Biological Engineering’s College of Fellows, an impressive group that represents the top two percent of medical and biological engineers. This distinction is one of the highest honors that can be bestowed upon a medical and biological engineer. Can you tell we are proud of him?!
We selected Dr. Antani to join our NLM family after a nationwide, competitive search, and his genius was readily apparent from the start. Dr. Antani’s career spans over two decades, during which he developed an innovative research portfolio focused on machine learning and artificial intelligence (AI). His lab at NLM focuses on using these tools to analyze enormous sets of biomedical data. Through this analysis, AI technology can “learn” to detect disease and assist health care professionals provide more efficient diagnoses. Examples of Dr. Antani’s work can be found in mobile radiology vehicles, which allow professionals to take chest X-rays and screen for HIV and tuberculosis using software containing algorithms developed in his lab. Check out the infographic below to learn more about the exciting research happening in Dr. Antani’s lab.
Now, in his own words, learn more about what makes Dr. Antani’s work so important!
What makes your team unique? Tell us more about the people working in your lab.
The postdoctoral research fellows, long-term staff scientists, and research scientists on my team explore challenging computational health topics while simultaneously advancing topics in machine learning for medical imaging. Dr. Ghada Zamzmi, Dr. Peng Guo, and Dr. Feng Yang bring expertise and drive to our lab. The scientists on my team, Dr. Zhiyun (Jaylene) Xue and Dr. Sivarama Krishnan Rajaraman, add over two decades of combined research and mentoring experience.
What do you enjoy about working at NLM?
There are many positives about working at NLM. At the top of the list is the encouragement and support to explore cutting-edge problems in medical informatics, data science, and machine intelligence, among other initiatives.
What is your advice for young scientists or people interested in pursuing a career in research?
I urge young scientists to recognize the power of multidisciplinary teams. I would also urge them to develop skills to clearly communicate their goals and research interests with colleagues who might be from a different domain so they can effectively collaborate and arrive at mutually beneficial results.
Where is your favorite place to travel?
I like to travel to places that exhibit the natural wonders of our planet. I hope to visit all our national parks someday.
When you’re not in the lab, what do you enjoy doing?
I am studying and exploring different aspects of music structure.
You’ve read his words, and now you can hear him for yourself! Follow our NLM YouTube page for more exciting content from the NLM staff that make it all possible. If you’d like to learn more about our IRP program, view job opportunities, and explore research highlights, I invite you to explore our recently redesigned NLM IRP webpage.
Transcript: [Antani]: I went to school for computer engineering in India. I’ve worked with image processing, computer vision, pattern recognition, machine learning. So my world was filled with developing algorithms that could extract interesting objects from images and videos. Pattern recognition is a family of techniques that looks for particular pixel characteristics or voxel characteristics inside an image and learns to recognize those objects. Deep learning is a way of capturing the knowledge inside an image and encapsulating it, and then researchers like me spend time advancing newer deep-learning networks that look more broadly into an image, recognizing these objects—recognizing organs, in my case, and diseases—and converting those visuals into numerical risk predictors that could be used by clinicians.
So my research is currently in three very different areas. One area looks at cervical cancer. A machine could look at the images and be a very solid predictor of the risk to the woman of developing cervical precancer, encouraging early treatment. Another area I work with [is] sickle cell disease. One of the risk factors in sickle cell disease is cardiac myopathy or cardiac muscle disease, which leads to stroke and perhaps even death. Looking at cardiac echo videos and using AI to be a solid predictor, along with other blood lab tests, improves the chances of survival.
A third area that I’m interested in is understanding the expression of tuberculosis [TB] in chest X-rays, particularly for children and those that are HIV-positive. The expression of disease in that subpopulation is very different from adults with TB who are not HIV positive. Every clinician has seen a certain number of patients in their clinical training. They perhaps have spent more time at hospitals or clinical centers, been exposed to a certain population, and they become very adept at that population. Machines, on the other hand, could be trained on data that is free of bias, from different parts of the world, different ethnicities, different age groups, so that there’s an improved caregiving and therefore, a better expectation on treatment and care.
Note: Transcript was modified for clarity.