A new deep learning algorithm can help x-ray readers interpret images more effectively and, among other things, help radiologists eliminate unnecessary imaging.
That’s according to a new study by a research team from Massachusetts General Hospital (MGH) and Seoul, South Korea-based Lunit, which developed the AI software.
According to the report, published at European Radiology, the new AI “had 28% sensitivity benefit for radiology residents, helping them properly recommend CT exams for potential lung cancer patients, and 30% specificity benefit for radiologists in lung cancer detection, reducing unnecessary CT exams.”
These gains translated into more appropriate CT orders, particularly for younger radiologists, and 30% fewer unnecessary scans.
"The use of AI could help to detect pulmonary nodules accurately with chest X-rays, as well as reduce the need for unnecessary chest CT exams in some patients," co-author Mannudeep K. Kalra, MD, a radiologist at MGH, said in a press release. "This finding can benefit patients by enabling them to avoid unneeded radiation exposure, and it can benefit the healthcare system by preventing certain medical costs.”
The Lunit tool was trained using more than 3 million pieces of medical data and is currently used in more than 300 hospitals to detect major chest diseases, including lung nodules and tuberculosis. Currently approved in over 30 countries, it’s expected to gain US FDA approval sometime this year.
For the study, researchers chose 519 patient scans from the National Lung Screening Trial. Three radiology residents and five board-certified rads interpreted the images, both with AI and without it.
With AI, residents recommended 28% more chest CTs (54.7% vs. 70.2%) for patients with visible lung cancer. Conversely, radiologists recommended nearly 30% fewer unnecessary scans for cancer-negative individuals (16.4% vs. 11.7%).
According to the authors, the software can ultimately help doctors diagnose patients with enhanced efficiency, spotting potential cancers earlier while saving time and costs for those who don’t require follow-up.
"Chest x-ray is the firsthand diagnostic tool to detect lung cancer, but it has limitations as it is a compressed 2D rendering of 3D human structures," said Brandon Suh, CEO of Lunit. "An accurate analysis through Lunit INSIGHT CXR can help medical professionals provide diagnosis to patients with increased efficiency --preventing potential cancer at an early stage, while saving time and cost for those who do not need a further examination.”
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