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Editor's e-Note
As image volumes continue to grow and outpace the number of radiologists, efficient workflow is essential in any radiology practice, not only to reduce burnout but also to optimize treatment. This month, we take a look at some AI advances that may help identify pathology earlier and ease workflow bottlenecks.

Enjoy the newsletter. Let us know what you think about it on Twitter and/or Facebook.

— Dave Yeager, editor
e-News Exclusive
AI May Improve Diabetes Diagnosis

Using a fully automated AI deep learning model, researchers were able to identify early signs of type 2 diabetes on abdominal CT scans, according to a recent study published in the journal Radiology. Type 2 diabetes affects approximately 13% of all adults in the United States, and an additional 34.5% of adults meet the criteria for prediabetes. Due to the slow onset of symptoms, it is important to diagnose the disease in its early stages. Some cases of prediabetes can last up to eight years, and an earlier diagnosis will allow patients to make lifestyle changes to alter the progression of the disease.

Abdominal CT imaging can be a promising tool to diagnose type 2 diabetes. CT imaging is already widely used in clinical practices, and it can provide a significant amount of information about the pancreas. Previous studies have shown that patients with diabetes tend to accumulate more visceral fat and fat within the pancreas than nondiabetic patients. However, not much work has been done to study the liver, muscles, and blood vessels around the pancreas, says study cosenior author Ronald M. Summers, MD, PhD, a senior investigator and a staff radiologist at the National Institutes of Health Clinical Center in Bethesda, Maryland.

"The analysis of both pancreatic and extrapancreatic features is a novel approach and has not been shown in previous work to our knowledge," says first author Hima Tallam, BSE, an MD/PhD student.

The manual analysis of low-dose noncontrast pancreatic CT images by a radiologist or trained specialist is a time-intensive and difficult process. To address these clinical challenges, there is a need for the improvement of automated image analysis of the pancreas, the authors say.

For this retrospective study, Summers and colleagues, in close collaboration with cosenior author Perry J. Pickhardt, MD, a professor of radiology at the University of Wisconsin School of Medicine and Public Health in Madison, used a data set of patients who had undergone routine colorectal cancer screening with CT at the University of Wisconsin Hospital and Clinics. Of the 8,992 patients who had been screened between 2004 and 2016, 572 had been diagnosed with type 2 diabetes and 1,880 with dysglycemia, a term that refers to blood sugar levels that go too low or too high. There was no overlap between diabetes and dysglycemic diagnosis.

Full story »
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