A Concise Review on Deep Learning for Musculoskeletal X-ray Images

Abstract

Musculoskeletal refers to the muscles and skeleton of the body. In particular, the musculoskeletal system contains joints, muscles, bones, cartilage, ligaments, bursae, and tendons. In addition, the body's movement is allowed by this system, and the musculoskeletal supports the stability of the body of a human being. Screening for musculoskeletal abnormalities is particularly critical as more than 1.7 billion people worldwide are affected by musculoskeletal conditions. Detecting whether a radiographic analysis is normal or abnormal is critical. The most common mistake in the emergency department is the incorrect diagnosis of fractures, which could lead to delayed treatment and temporal/permanent disability. According to the latter, we can find several studies showing how a deep learning (DL) system can accurately detect fractures in the musculoskeletal system. This paper aimed to review the specific impact of using DL for musculoskeletal X-ray imaging. As far as we know, this is the first review focusing on the topic. In particular, this revision supports a more extensive study of the most significant aspects of machine learning (ML) and DL is dealing with it. It introduced the importance of using DL methods in musculoskeletal X-ray imaging and described MURA (musculoskeletal radiographs) dataset as an example. Specifically, convolutional neural networks (CNNs) are identified as one of the most widely adopted solutions within DL, and several enhancements have been described. Finally, current open challenges and suggested solutions are presented to help researchers propose new developments.

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