AI improves performance of nonradiologists in chest imaging

AI improves chest x-ray imaging interpretation by nonradiologist practitioners, which could be useful in low-resource settings, according to research published January 29 in Chest. A team led by Jan Rudolph, MD, from University Hospital, LMU Munich in Germany found that a convolutional neural network (CNN)-based AI system focusing on chest x-rays improved the performance of nonradiologists in diagnosing several chest pathologies. “In an emergency unit setting without 24/7 radiology coverage, the presented AI solution features an excellent clinical support tool to nonradiologists, similar to a second reader, and allows for a more accurate primary diagnosis and thus earlier therapy initiation,” the Rudolph team wrote. Chest x-rays are the go-to modality for assessing whether or not a disease requires immediate treatment. But determining this isn’t easy, with experts needed to evaluate the presence of projection phenomena, superimpositions, and similar representations of different findings. That last part can be challenging for nonradiologists who do not constantly interpret diagnostic imaging exams. Still, they may be tasked with clinical decision-making based on such findings in emergency settings without radiologists being there all the time. Previous studies have explored AI’s potential in interpreting chest x-rays and thus, helping to streamline clinical workflows and improve patient care. Rudolph and co-authors evaluated the performance of an AI algorithm tha...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: Digital X-Ray Source Type: news