Explore more about: Image Processing, Visual Perception and Display

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Traditional medical imaging works great for people with light skin but has trouble getting clear pictures from patients with darker skin. A Johns Hopkins University–led team found a way to deliver clear pictures of anyone's internal anatomy, no matter their skin tone.  Source: Johns Hopkins University
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NIBIB-funded engineers are using deep learning to differentiate tumor more accurately from normal tissue in positron emission tomography (PET) images.
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A team of NIH microscopists and computer scientists used a type of artificial intelligence called a neural network to obtain clearer pictures of cells at work even with extremely low, cell-friendly light levels.
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A team of engineers has demonstrated how a new algorithm they developed was able to successfully predict whether or not a COVID-19 patient would need ICU intervention. This artificial intelligence-based approach could be a valuable tool in determining a proper course of treatment for individual patients.
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Engineers have demonstrated how a deep learning algorithm can be applied to a conventional computerized tomography (CT) scan in order to produce images that would typically require a higher level of imaging technology known as dual-energy CT.
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The National Institutes of Health has launched an ambitious effort to use artificial intelligence, computation, and medical imaging to enable early disease detection, inform successful treatment strategies, and predict individual disease outcomes of COVID-19.
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A new $2.3 million grant from the NIBIB at NIH will support a research effort led by Rensselaer Polytechnic Institute to make a virtual surgery scenario – and others like it – a reality.

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NIBIB-funded researchers at Stanford University have created an artificial neural network that analyzes lung CT scans to provide information about lung cancer severity that can guide treatment options.