National Institutes of Health (NIH)

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AI to help researchers see the bigger picture in cell biology

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Biological engineering, Biology, Broad Institute, Cells, Computer science and technology, Diagnostics, Electrical engineering and computer science (EECS), Genetics, Health, IDSS, Laboratory for Information and Decision Systems (LIDS), Machine Learning, Medicine, MIT Schwarzman College of Computing, National Institutes of Health (NIH), Research, School of Engineering

Studying gene expression in a cancer patient’s cells can help clinical biologists understand the cancer’s origin and predict the success of different treatments. But cells are complex and contain many layers, so how the biologist conducts measurements affects which data they can obtain. For instance, measuring proteins in a cell could yield different information about the […]

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AI algorithm enables tracking of vital white matter pathways

ai, AI (Artificial Intelligence), Algorithms, Alzheimer's, Artificial Intelligence, Brain and cognitive sciences, Department of Defense (DoD), Health care, Health sciences and technology, Imaging, Institute for Medical Engineering and Science (IMES), National Institutes of Health (NIH), Neuroscience, Parkinson's, Picower Institute, Research, School of Engineering, School of Science

The signals that drive many of the brain and body’s most essential functions — consciousness, sleep, breathing, heart rate, and motion — course through bundles of “white matter” fibers in the brainstem, but imaging systems so far have been unable to finely resolve these crucial neural cables. That has left researchers and doctors with little

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Deep-learning model predicts how fruit flies form, cell by cell

3-D, Animals, Artificial Intelligence, Biology, Cells, Computer vision, Imaging, Machine Learning, Mechanical engineering, National Institutes of Health (NIH), Research, School of Engineering, Self-assembly

During early development, tissues and organs begin to bloom through the shifting, splitting, and growing of many thousands of cells.A team of MIT engineers has now developed a way to predict, minute by minute, how individual cells will fold, divide, and rearrange during a fruit fly’s earliest stage of growth. The new method may one

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New AI system could accelerate clinical research

Artificial Intelligence, Broad Institute, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Health, Health care, Imaging, Machine Learning, Medicine, MIT Schwarzman College of Computing, National Institutes of Health (NIH), Research, School of Engineering

Annotating regions of interest in medical images, a process known as segmentation, is often one of the first steps clinical researchers take when running a new study involving biomedical images.For instance, to determine how the size of the brain’s hippocampus changes as patients age, the scientist first outlines each hippocampus in a series of brain

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