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Sameer Antani and Artificial Intelligence

This video is about Dr. Sameer Antani and his work on applying artificial intelligence, especially pattern recognition and deep learning, to cervical cancer, sickle cell disease, and tuberculosis. Learn more about Dr. Antani here: https://www.nlm.nih.gov/research/rese... An audio-described version of the video is available here:    • (Audio Described) Sameer Antani and A...   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 pre-Cancer, encouraging early treatment. Another area I work with, 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 in chest x-rays, particularly for children and those that are HIV-positive. The expression of disease in that sub-population is very different from adults with TB, who are not HIV positive. Every clinician have seen a certain number of patients in their clinical training. They perhaps have spent more time at hospitals or clinical centers, and exposed to 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 care giving and therefore, a better expectation on treatment and care. #audiodescription #audiodescriptions