Cone beam CT of the musculoskeletal system : clinical applications

Objectives: The aim of this pictorial review is to illustrate the use of CBCT in a broad spectrum of musculoskeletal disorders and to compare its diagnostic merit with other imaging modalities, such as conventional radiography (CR), Multidetector Computed Tomography (MDCT) and Magnetic Resonance Imaging. Background: Cone Beam Computed Tomography (CBCT) has been widely used for dental imaging for over two decades. Discussion: Current CBCT equipment allows use for imaging of various musculoskeletal applications. Because of its low cost and relatively low irradiation, CBCT may have an emergent role in making a more precise diagnosis, assessment of local extent and follow-up of fractures and dislocations of small bones and joints. Due to its exquisite high spatial resolution, CBCT in combination with arthrography may be the preferred technique for detection and local staging of cartilage lesions in small joints. Evaluation of degenerative joint disorders may be facilitated by CBCT compared to CR, particularly in those anatomical areas in which there is much superposition of adjacent bony structures. The use of CBCT in evaluation of osteomyelitis is restricted to detection of sequestrum formation in chronic osteomyelitis. Miscellaneous applications include assessment of (symptomatic) variants, detection and characterization of tumour and tumour-like conditions of bone. Teaching Points: Review the spectrum of MSK disorders in which CBCT may be complementary to other imaging techniques. Compare the advantages and drawbacks of CBCT compared to other imaging techniques. Define the present and future role of CBCT in musculoskeletal imaging

Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithm

Hospital emergency departments frequently receive lots of bone fracture cases, with pediatric wrist trauma fracture accounting for the majority of them. Before pediatric surgeons perform surgery, they need to ask patients how the fracture occurred and analyze the fracture situation by interpreting X-ray images. The interpretation of X-ray images often requires a combination of techniques from radiologists and surgeons, which requires time-consuming specialized training. With the rise of deep learning in the field of computer vision, network models applying for fracture detection has become an important research topic. In this paper, YOLOv8 algorithm is used to train models on the GRAZPEDWRI-DX dataset, which includes X-ray images from 6,091 pediatric patients with wrist trauma. The experimental results show that YOLOv8 algorithm models have different advantages for different model sizes, with YOLOv8l model achieving the highest mean average precision (mAP 50) of 63.6\%, and YOLOv8n model achieving the inference time of 67.4ms per X-ray image on one single CPU with low computing power. In this way, we create "Fracture Detection Using YOLOv8 App" to assist surgeons in interpreting X-ray images without the help of radiologists. Our implementation code is released at https://github.com/RuiyangJu/Bone_Fracture_Detection_YOLOv8

DeepLOC: Deep Learning-based Bone Pathology Localization and Classification in Wrist X-ray Images

In recent years, computer-aided diagnosis systems have shown great potential in assisting radiologists with accurate and efficient medical image analysis. This paper presents a novel approach for bone pathology localization and classification in wrist X-ray images using a combination of YOLO (You Only Look Once) and the Shifted Window Transformer (Swin) with a newly proposed block. The proposed methodology addresses two critical challenges in wrist X-ray analysis: accurate localization of bone pathologies and precise classification of abnormalities. The YOLO framework is employed to detect and localize bone pathologies, leveraging its real-time object detection capabilities. Additionally, the Swin, a transformer-based module, is utilized to extract contextual information from the localized regions of interest (ROIs) for accurate classification.Comment: AIST-2023 accepted pape