Machine learning-based 'red dot' triage system shows promise for optimizing radiologist workload

The researchers tackled this with a "red dot" approach--one that's been around for more than four decades and traditionally involves marking abnormal plain film with circular, red stickers. The method has historically achieved 78 percent sensitivity and 91 percent specificity across pooled chest and abdominal exams, Yates et al. said, but it doesn't relieve the issue of backlog in the reporting room. "Existing machine learning approaches have typically focused on formal reporting of pathology or multi-label classification, rather than prioritization based on abnormality," the authors wrote. "The aim of the present study was to explore the ability of a deep learning, computer vision approach in binary normality classification of plain film chest radiographs to serve as a rapid screening tool." Yates' team applied the red dot system to machine learning using deep convolutional neural networks (CNNs), according to the study.