From the November 2020 issue of HealthCare Business News magazine
In April, the RSNA and the American Society of Neuroradiology (ASNR), along with more than 60 volunteer neuroradiologists, unveiled what they’re calling
the largest public collection of expert-annotated brain hemorrhage CT scans.
The collection, intended to speed up and support the creation of machine learning algorithms for diagnosing and characterizing brain hemorrhage, is a product of the latest edition of the RSNA Artificial Intelligence Challenge, in which participants were tasked with creating an algorithm to assist in identifying and characterizing intracranial hemorrhages from brain CT scans. The development process and observations of the data set were recorded in a paper published in Radiology: Artificial Intelligence.
“The value of this challenge is to create a data set that might lead to a generalizable solution, and the best way to do that is to train a model from data originating from multiple institutions that use a variety of CT scanners from various manufacturers, scanning protocols and a heterogeneous patient population,” said the paper’s lead author, Dr. Adam Flanders, a neuroradiologist and professor at Thomas Jefferson University Hospital, in a statement.
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The release of the data set led to more than 22,000 submissions from 1,787 individual competitors in 1,345 teams from 75 countries in the challenge. The organizers expect its development by RSNA and a subspecialty like ASNR will help foster future collaborations.
“I was really impressed by the huge volunteer effort and the tremendous worldwide interest in this project,” Flanders said. “The data set we created for this challenge will endure as a valuable machine learning research resource for years to come.”
The data set will be used again in this year’s competition, which will be a collaboration between RSNA and the Society of Thoracic Radiology to detect and characterize pulmonary embolisms on chest CTs.
USPSTF issues draft proposal expanding lung cancer screening access
The U.S. Preventive Services Task Force (USPSTF)
expanded the eligibility criteria for low-dose CT lung cancer screenings in July with a draft recommendation lowering the age for testing people at high risk due to their smoking history from 55 to 50. It also recommended reducing the pack-years of smoking history that make an individual eligible for screening from 30 pack years to 20.
