Date

September 30, 2024

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An AI algorithm used on a CT image of the abdomen shows body composition that can reveal emerging health problems.

Mining Medical Scans for Clues to Prevent & Detect Diseases

Every year, more than 80 million computerized tomography (CT) scans are performed in the United States. These diagnostic exams use X-ray technology to generate detailed anatomical images, enabling clinicians to identify tumors, fractures, and other abnormalities.

 

New York University (NYU) Langone Health’s Department of Radiology is convinced that the mountain of valuable data contained in these images remains largely untapped. A team of faculty, led by Miriam A. Bredella, M.D., M.B.A., the Bernard and Irene Schwartz Professor of Radiology and director of the Clinical and Translational Science Institute; Soterios Gyftopoulos, M.D., M.B.A., M.Sc., chief of radiology at NYU Langone Hospital—Brooklyn; and Bari Dane, M.D., the department’s director of computerized tomography, uses information from “discarded” clinical CTs to uncover emerging health problems, a process called opportunistic imaging.

 

Advances in both artificial intelligence, or AI, and machine learning have allowed radiologists to quantify the amount of fat, muscle, mineral deposits in veins and arteries (known as vascular calcification), and bone mineral density in each image. “There are new things we’re discovering every day about the potential these images hold for disease prevention and detection,” says Dr. Dane, who oversees the technical component of harnessing CT scans for opportunistic imaging…

 

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