• Average volume difference for lesions calculated by model with those calculated by radiologists: 0.25 ml “Our algorithm, which is an end-to-end segmentation model, can be easily deployed and applied for other segmentation tasks as well,” Hwang said. As their algorithm has so far only been tested on internal datasets, the researchers now plan to perform external validation of their model, Hwang added. “Moreover, we plan to optimize our model in order to be more sensitive to small lesions,” he said.
“This work shows the promise of AI systems to add value to clinical care by extracting new information from existing imaging data,” said presenter Dr. Kirti Magudia, PhD, in a statement from the RSNA. “The deployment of AI systems would allow radiologists, cardiologists and primary care doctors to provide better care to patients at minimal incremental cost to the health care system.” Magudia, currently an abdominal imaging and ultrasound fellow at the University of California, San Francisco, shared the results of research performed while she was a radiology resident at Brigham and Women’s Hospital and part of a multidisciplinary group that developed the deep-learning algorithm.
AI abdominal fat analysis assesses cardiovascular risk By Erik L. Ridley, AuntMinnie staff writer
Body composition metrics automatically calculated from abdominal CT exams by an artificial intelligence (AI) algorithm are significantly associated with patient risk for future major cardiovascular events, according to a Wednesday morning presentation at the RSNA 2020 virtual meeting. After testing their deep-learning model on abdominal CT exams from over 12,000 patients, researchers from Brigham and Women’s Hospital in Boston found that its visceral fat measurements were independently associated with subsequent myocardial infarction and subsequent stroke. Body mass index (BMI), however, was not.
Radiology Reporter, Copyright © 2020 AuntMinnie.com
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