Diagnostic Reading #34: Five “Must Read” Articles on Medical Imaging
Reading Time: 3 minutes read
Reporting radiology findings more efficiently; and bringing mobile X-ray to COVID frontlines are in the news.
This week’s articles in Diagnostic Reading include: AI algorithm highly accurate for identifying COVID; bringing mobile imaging to COVID frontlines; CT method details CPR; successful new reporting tool; and outpatient imaging centers lag in recovery.
AI algorithm may be generalizable for spotting COVID-19 – AuntMinnie
An artificial intelligence (AI) algorithm trained on a large dataset can be highly accurate for identifying COVID-19 pneumonia on chest CT scans, according to research recently published in Nature Communications. It also can be highly specific for non-COVID-19-related pneumonias. The researchers aimed to maximize the potential for generalizability by utilizing a diverse, multinational dataset, which included COVID-19 patients from four hospitals in China, Italy and Japan.
Deploying mobile X-ray vehicles to COVID-19 frontlines – Everything Rad
To help reduce the transmission of COVID-19, and to bring care closer to patients infected with the virus, the government in Singapore built several Community Care Facilities. An integral part of the care was chest imaging. Read the blog by NHGD to learn how they outfitted mobile X-ray trailers to bring chest imaging to the CCF sites. Read the special series on the impact of COVID-19 in radiology.
New 3D CT scanning method shows what happens during CPR – Healthcare-in-Europe
As part of an international collaboration, researchers from Aarhus University have developed a dynamic 3D CT scanning method that shows what happens inside the body during simulated heart massage. This method can be compared with a stop-motion video production, but where each image in the video has been replaced by a complete 3D CT scan. The method reproduces the organs movements during heart massage in a very detailed way and makes it possible to perform advanced imaging analysis on the volumetric dynamic CT dataset.
Radiologists reveal ‘RADCAT’ system to categorize imaging reports, convey important findings – Health Imaging
Radiologists face the challenge of not only accurately interpreting exams but efficiently communicating their findings to care teams. To help ease this burden, experts recently proposed a structured reporting system—RADCAT—published in the Journal of the American College of Radiology. This radiology report categorization tool, in the same vein of reporting systems like BI-RADS, organizes imaging documents and relays findings using an automated communication system. So far, it’s been a success.
Outpatient imaging centers lag behind main hospitals in COVID-19 volume recovery – Diagnostic Imaging
Not only did non-hospital-based imaging centers experience the most significant drop in imaging volumes during the peak of the COVID-19 pandemic, but these off-site facilities also have been the slowest to recover, according to a study published in Academic Radiology. There are several possible reasons why a center might recover more slowly, including a slower re-opening of affiliated imaging centers compared to large urban hospitals.
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