Can ‘normal filtering’ AI save radiologists time?

AI algorithms appear to have clinical value based on detecting normal x-rays – that is, by flagging chest x-rays as normal versus abnormal, they may reduce reading times for radiologists, according to research presented recently at the RSNA meeting in Chicago. In a session on chest imaging, scientists from AI developers Lunit and DeepTek.ai presented separate studies of AI algorithms configured to segregate patient chest x-ray images into those with significant clinical findings and those considered normal. In both cases, the algorithms ruled out a potentially significant number of reads. “Actually, what we want to do is spend time on non-plain radiographs now, but then what should we do for the chest x-rays?” Lee said. Lee and colleagues tested a newly designed algorithm trained on about 400,000 chest x-rays from various sources. The model was trained to consider any finding, including medical devices, incorrect patient positioning, or poor image quality as abnormal, given that it is “actually impossible to define a single definition of what is normal,” Lee noted. The model was then evaluated on three independent retrospective external data sets that included a total of 8,505 images, with 3,567 labeled as normal and 3,853 labeled as abnormal. According to the analysis, the algorithm correctly classified 22% of all chest x-rays (n = 1,765) as normal, and these cases could be potentially removed from formal reporting, with the AI serving as a second reader, Le...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: Subspecialties Chest Radiology Source Type: news