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This interview with Maryellen Giger, PhD, delves into the creation of the MIDRC imaging repository, how its data can be used to develop and evaluate AI algorithms, ways that bias can be introduced—and potentially mitigated—in medical imaging models, and what the future may hold.
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Researchers have shown that an automated cancer diagnostic method, which pairs cutting-edge ultrasound techniques with artificial intelligence, can accurately diagnose thyroid cancer, of which there are more than 40,000 new cases every year.
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NIBIB-funded researchers are fine-tuning a wearable, cuffless blood pressure monitor. Made of graphene, one of the thinnest materials in the world, the device is worn on the underside of the wrist and can measure blood pressure with comparable accuracy to a standard blood pressure cuff.
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Researchers have found that AI models could accurately predict self-reported race in several types of medical images, suggesting that race information could be unknowingly incorporated into image analysis models.
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NIBIB-funded researchers are developing an autonomous robot that can perform bowel surgery with minimal assistance from a surgeon. In preclinical models, the robot outperformed expert surgeons when compared head-to-head.
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NIH-funded researchers are investigating how to use smartwatches to predict clinical test results, which could potentially serve as an early warning signal for underlying health issues.
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Washington University in St. Louis is joining a major international effort to advance data science, catalyze innovation and spur health discoveries across Africa. The program is supported by the National Institutes of Health’s (NIH) Common Fund, which will invest nearly $75 million over five years to fund the Harnessing Data Science for Health Discovery and Innovation in Africa (DS-I Africa) program. Source: Washington University in St. Louis
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The Medical Imaging and Data Resource Center (MIDRC), a collaboration of leading medical imaging organizations funded by NIBIB, has launched a repository of imaging data to facilitate medical imaging research on COVID-19.
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Compared to standard machine learning models, deep learning models are largely superior at discerning patterns and discriminative features in brain imaging, despite being more complex in their architecture.
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A new article looks at the use of virtual imaging trials in effective assessment and optimization of CT and radiography acquisitions and analysis tools to help manage the coronavirus disease (COVID-19) pandemic.