Artificial Intelligence in Musculoskeletal Imaging: Review of Current Literature, Challenges, and Trends

Abstract

Artificial intelligence (AI) has gained major attention with a rapid increase in the number of published articles, mostly recently. This review provides a general understanding of how AI can or will be useful to the musculoskeletal radiologist. After a brief technical background on AI, machine learning, and deep learning, we illustrate, through examples from the musculoskeletal literature, potential AI applications in the various steps of the radiologist's workflow, from managing the request to communication of results. The implementation of AI solutions does not go without challenges and limitations. These are also discussed, as well as the trends and perspectives.

The Use of Artificial Intelligence in the Evaluation of Knee Pathology

“…Additional questions inherent to relying on the results of ML algorithms for medical decision making have yet to be fully addressed including issues surrounding medical liability, public perception, and trust in removing the human element from some aspects of medical image interpretation. 4…”

“…3 The integration of artificial intelligence (AI) algorithms into the workflow of MSK radiology holds the potential to improve diagnostic accuracy, expedite cases with urgent findings, reduce reader fatigue, and provide decision support where radiology expertise is unavailable. 4 In the diagnostic evaluation of knee pathology, most AI literature has focused on building convolutional neural networks (CNNs) that can perform a single interpretive task under the categories of pathology detection (ligament or meniscus tear, cartilage lesion), classification (assign osteoarthritis grading to knee radiographs, classify meniscus tears), and segmentation (cartilage and meniscus segmentation) (►Fig. 1).…”

Quantitative Imaging in Inflammatory Arthritis: Between Tradition and Innovation

“…138 The application of deep and machine-learning algorithms has already also demonstrated good results in the musculoskeletal field, to diagnose, for instance, cartilage lesions or meniscal tears in the knee and to determine skeletal maturity in children. [139][140][141] In 2013, Pfeil and colleagues had already proposed a computer-aided joint space analysis to evaluate joint narrowing in the metacarpophalangeal and proximal interphalangeal joints using radiographs of patients with RA, obtaining high sensitivity and specificity, particularly in the former joint (88.1% and 77.8%, respectively). 142 This type of technological improvement is also associated with the possibility of performing complex texture analyses.…”

Artificial Intelligence Explained for Nonexperts

“…A comprehensive review of applications of deep learning in the field of musculoskeletal imaging is available in Gyftopoulos et al, 65 Hirschmann et al, 66 and Burns et al 67 For more background on the development of deep learning, LeCun and colleagues 68 provide an in-depth review. For an extensive commentary on the pitfalls and challenges of AI applications, Riley 69 and Wiens et al 70 cover different focus areas, all of which are necessary and basic knowledge for anyone who makes decisions about using AI in practice.…”