Meet the NLM Investigators: For Sameer Antani, PhD, Seeing is More Than Meets the Eye

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.

Infographic titled: Seeing is more than meets the eye. Under the title the investigator's name, title and division are listed as: Sameer Antani, PhD, Investigator, Computational Health. 

The first column of the infographic is titled: Projects. Two bullets are listed in the first column. The first bullet reads: Discovering the impact of data on automated AI and machine learning (AI/ML) processes on diagnostics. The second bullet says: Improving AI/ML algorithm decisions to be consistent, reproducible, portable, explainable, unbiased, and representative of severity.

The second column is titled: Process. The first bullet in this column reads: Using images and videos alongside AIML technology to identify and diagnose:
Cancers: Cervical, Oral, Skin (Kaposi Sarcoma)
Cardiomyopathy 
Cardiopulmonary diseases. 
The second bullet reads: Analyzing a variety of image types, including:
Computerized Tomography (CT), Magnetic Resonance Imaging (MRI), X-ray, ultrasound, photos, videos, microscopy. 

The third and final column in the infographic is titled: What It Looks Like. In this column there are four images of chest x-rays illustrating the detection of HIV and TB.

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.

YouTube: Sameer Antani and Artificial Intelligence

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.

Informing Success from the Outside In: Introducing the NLM Board of Regents CGR Working Group

Guest post by Valerie Schneider, PhD, staff scientist at the National Library of Medicine (NLM) National Center for Biotechnology Information (NCBI), National Institutes of Health (NIH), and Kristi Holmes, PhD, Director of Galter Health Sciences Library & Learning Center and Professor of Preventive Medicine at Northwestern University Feinberg School of Medicine.

Last year, we described how NLM is developing the NIH Comparative Genomics Resource (CGR)—a project that offers content, tools, and interfaces for genomic data resources associated with eukaryotic research organisms—in two blog posts:

Eukaryote refers to any single-celled or multicellular organisms whose cell contains a distinct and membrane-bound nucleus. Since eukaryotes all likely evolved from the same common ancestry, studying them can grant us insight into how other eukaryotes—including those in humans—work and makes CGR and its resources that much more important to eukaryotic research.

CGR aims to:

  • Promote high-quality eukaryotic genomic data submission.
  • Enrich NLM’s genomic-related content with community-sourced content.
  • Facilitate comparative biological analyses.
  • Support the development of the next generation of scientists.

Since our last two posts, the team at NCBI has been hard at work making important technical and content updates to and socializing CGR’s suite of tools. For instance, they published new webpages that organize genome-related data by taxonomy, making it available for browsing and immediate download. They also created the ClusteredNR Database, a new database for the Basic Local Alignment Search Tool (BLAST), to provide results with greater taxonomic context for sequence searches, and incorporated new gene information from the Alliance of Genome Resources, an organization that unites data and information for model organisms’ unique aspects, into Gene. NCBI is also engaging with genomics communities to understand their needs and requirements for comparative genomics through the NLM Board of Regents Comparative Genomics Working Group.

The working group is lending their perspective and extensive expertise to the project, activities that are essential to CGR’s success and development. We have charged working group members with guiding the development of a new approach to scientific discovery that relies on genomic-related data from research organisms, helping project teams keep pace with changes in the field, and understanding the scientific community’s needs and expectations for key functionalities. To do this, working group members help NLM set development priorities such as exploring CGR’s integration with existing infrastructures and related workforce development opportunities.

Projects like CGR highlight how critical interdisciplinary collaboration is to modern research and how success requires community perspectives and involvement. Working group members will be sharing more information about this project at upcoming conferences and in biomedical literature, and our team at NCBI will also share events and resources through our NIH Comparative Genomics Resource website.

If you are a member of a model organism community, are working on emerging eukaryotic research models, or support eukaryotic genomic data—whether you are a researcher, educator, student, scholarly society member, librarian, data scientist, database resource manager, developer, epidemiologist, or other stakeholder in our progress—we encourage you to reach out and get involved. Here are a few suggestions:

  • Invite us to join you at a conference, teach a workshop, partner on a webinar, or discuss other ideas you may have to foster information sharing and feedback.
  • Use and share CGR’s suite of tools and share your feedback.
  • Be on the lookout for project updates and events on the CGR website or follow @NCBI on Twitter.

We’re always excited to get feedback through CGR listening sessions and user testing for tool and resource updates. Email cgr@nlm.nih.gov to learn all the ways you can participate.

Thank you to the members of the NLM Board of Regents CGR Working Group!

Alejandro Sanchez Alvarado, PhD

Executive Director and Chief Scientific Officer
Priscilla Wood Neaves Chair in the Biomedical Sciences
Stowers Institute for Medical for Medical Research

Hannah Carey, PhD
Professor, Department of Comparative Biosciences, School of Veterinary Medicine
University of Wisconsin-Madison

Wayne Frankel, PhD
Professor, Department of Genetics & Development
Director of Preclinical Models, Institute of Genomic Medicine
Columbia University Medical Center

Kristi L. Holmes, PhD (Chair)
Director, Galter Health Services Library & Learning Center
Professor of Preventive Medicine (Health & Biomedical Informatics)
Northwestern University Feinberg School of Medicine

Ani W. Manichaikul, PhD
Associate Professor, Center for Public Health Genomics
University of Virginia School of Medicine

Len Pennacchio, PhD
Senior Scientist
Lawrence Berkeley National Laboratory

Valerie Schneider, PhD (Executive Secretary)
Program Head, Sequence Enhancements, Tools and Delivery (SeqPlus)
HHS/NIH/NLM/NCBI

Kenneth Stuart, PhD
Professor, Center of Global Infectious Disease Research
Seattle Children’s Research Institute

Tandy Warnow, PhD
Grainger Distinguished Chair in Engineering
Associate Head of Computer Science
University of Illinois, Champaign-Urbana

Rick Woychik, PhD (NIH CGR Steering Committee Liaison)
Director, National Institute of Environmental Health Sciences (NIEHS) and the National Toxicology Program (NTP)

Cathy Wu, PhD
Unidel Edward G. Jefferson Chair in Engineering and Computer Science
Director, Center for Bioinformatics & Computational Biology
Director, Data Science Institute
University of Delaware

Dr. Schneider is the deputy director of Sequence Offerings and the head of the Sequence Plus program. In these roles, she coordinates efforts associated with the curation, enhancement, and organization of sequence data, as well as oversees tools and resources that enable the public to access, analyze, and visualize biomedical data. She also manages NCBI’s involvement in the Genome Reference Consortium, which is the international collaboration tasked with maintaining the value of the human reference genome assembly.

Dr. Holmes is dedicated to empowering discovery and equitable access to knowledge through the development of computational and social architectures to support these goals. She also serves on the leadership team of the Northwestern University Clinical and Translational Sciences Institute.

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