Diagnostic Reading #8: Five “Must Read” Articles on HIT and Radiology
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Radiology’s role in diagnosing rare diseases; and CT’s role in diagnosing migraines are in the news.
This week’s articles in Diagnostic Reading include: AI/image analysis ranked No. 1 digital health technology; emergency department CTs may not benefit most migraine patients; radiology’s role in diagnosing rare diseases; AI successfully classifies elbow abnormalities; and MRI technique may diagnose fatty liver disease.
AI-powered image analysis ranked 2019’s top digital health technology – Radiology Business
According to a new ranking from Forbes, artificial intelligence (AI) and its ability to help radiologists streamline image analysis represents the No. 1 digital health technology of 2019. The list started with a survey of companies worldwide, with each respondent asked to “indicate the key technology which you believe will have the most profound impact on the healthcare industry during 2019.” Big data analytics was the top answer, followed by AI at No. 2.
Although Variant 3 of the American College of Radiology (ACR) Appropriateness Criteria recommends performing non-contrast head CT (NCCT) on patients with sudden severe headache who do not present other high-risk features, researchers from the department of radiology at Johns Hopkins Medical Center in Baltimore, Maryland found that performing emergency department (ED) NCCT scans on these patents has little value. These findings are detailed in a recent study in the Journal of the American College of Radiology.
The power of medical imaging for rare diseases – Everything Rad
Sometimes, patients suffering from a rare disease experience a delayed diagnosis. The delay can enable their condition to further advance, possibly jeopardizing their quality of life and, in some cases, life expectancy. By the time the correct diagnosis has been determined, these patients have usually been forced to visit several different doctors, adding to their frustration and uncertainty, and to their loved ones. Fortunately, radiology can help diagnose rare diseases in some cases, helping patients get treatment as early as possible.
AI classifies pediatric elbow abnormalities with 88% accuracy – AI in Healthcare
A deep-learning model classified acute and nonacute pediatric elbow abnormalities on radiographs in trauma with 88 percent accuracy, according to recently published research in Radiology: Artificial Intelligence. The research team sought to determine the feasibility of using deep learning with a multiview approach for pediatric elbow abnormalities on radiographs—similar to how radiologists review multiple images at their workstations.
University of Arizona in Tucson researchers have developed an MRI technique aimed to replace blood tests and invasive biopsies for measuring nonalcoholic fatty liver disease, according to a report by the Arizona Daily Star. The technology can determine the percentage of liver fat before symptoms appear. Particularly, the technique may help diagnose the disease earlier in Hispanics who are disproportionately affected by the disease due to obesity often caused by cultural dietary habits, socioeconomic status and limited access to healthcare.