  Diagnosing and treating abnormalities in the wrist, specifically distal
radius, and ulna fractures, is a crucial concern among children, adolescents,
and young adults, with a higher incidence rate during puberty. However, the
scarcity of radiologists and the lack of specialized training among medical
professionals pose a significant risk to patient care. This problem is further
exacerbated by the rising number of imaging studies and limited access to
specialist reporting in certain regions. This highlights the need for
innovative solutions to improve the diagnosis and treatment of wrist
abnormalities. Automated wrist fracture detection using object detection has
shown potential, but current studies mainly use two-stage detection methods
with limited evidence for single-stage effectiveness. This study employs
state-of-the-art single-stage deep neural network-based detection models
YOLOv5, YOLOv6, YOLOv7, and YOLOv8 to detect wrist abnormalities. Through
extensive experimentation, we found that these YOLO models outperform the
commonly used two-stage detection algorithm, Faster R-CNN, in fracture
detection. Additionally, compound-scaled variants of each YOLO model were
compared, with YOLOv8m demonstrating a highest fracture detection sensitivity
of 0.92 and mean average precision (mAP) of 0.95. On the other hand, YOLOv6m
achieved the highest sensitivity across all classes at 0.83. Meanwhile, YOLOv8x
recorded the highest mAP of 0.77 for all classes on the GRAZPEDWRI-DX pediatric
wrist dataset, highlighting the potential of single-stage models for enhancing
pediatric wrist imaging.
