|Overview||They reviewed how pattern recognition in MSK imaging using AI could facilitate the diagnosis of bone tumors, detection of bone metastases, evaluation of pediatric bone age, identification of fractures, labeling of images, and assessment of OA.
|Authors||Natalia Gorelik, Jaron Chong, Dana J. Lin
|Journal||Semin Musculoskelet Radiol 2020;24:38–49.|
|Recommendation/Comment||This article is of interest to sonographers expert in ultrasound MSK|
|Clinical implication||The article helps to understand the applications of AI in the diagnosis of MSK pathologies. Future research will no doubt further expand on the variety of MSK pathologies that can be addressed with AI-based solutions.|
|Ultrasound speciality||Musculoskeletal sonography – orthopaedics and traumatology|
Abstract: Artificial intelligence (AI) has the potential to affect every step of the radiology workflow, but the AI application that has received the most press in recent years is image interpretation, with numerous articles describing how AI can help detect and characterize abnormalities as well as monitor disease response. Many AI-based image interpretation tasks for musculoskeletal (MSK) pathologies have been studied, including the diagnosis of bone tumors, detection of osseous metastases, assessment of bone age, identification of fractures, and detection and grading of osteoarthritis. This article explores the applications of AI for image interpretation of MSK pathologies.
Keywords: musculoskeletal, deep learning, neural network