AI proposed to help radiologists detect child abuse injuries

A team at Michigan State University has developed an AI model designed to detect rib fractures in children under three years old, with the ribcage being the most common fracture site in abused children. The model approached the capabilities of expert human readers on a set of 1,109 pediatric chest x-rays and could serve as a tool to help detect injuries due to physical abuse, noted lead author Jonathan Burkow, a doctoral  student, and colleagues. “Rib fractures are extremely important to detect as they are a sentinel injury for physical abuse in young children that portend poor outcomes; a single rib fracture in children is associated with a 2.5 times increase in mortality rate,” the group wrote. The study was published April 10 in Scientific Reports. Detecting rib fractures in pediatric x-rays is challenging, as they can be obliquely oriented to the imaging detector or obscured by other structures, the authors explained. Even expert, specialized radiologists performing their first reads on x-rays can miss up to two-thirds of all rib fractures, they added. An AI model that acts as a first-read “augmentation technique” could improve both detection of rib fractures and the speed of image interpretation, especially for non-expert readers, the authors suggested. To that end, the group adapted two convolutional neural network (CNN) architectures, RetinaNet and YOLOv5, and a new decision scheme they called “avalanche decision.” This scheme dynamically reduces the ...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: Digital X-Ray Source Type: news