Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications

Artificial intelligence (AI) is increasingly being studied in musculoskeletal oncology imaging. AI has been applied to both primary and secondary bone tumors and assessed for various predictive tasks that include detection, segmentation, classification, and prognosis. Still, in the field of clinical research, further efforts are needed to improve AI reproducibility and reach an acceptable level of evidence in musculoskeletal oncology. This review describes the basic principles of the most common AI techniques, including machine learning, deep learning and radiomics. Then, recent developments and current results of AI in the field of musculoskel-etal oncology are presented. Finally, limitations and future perspectives of AI in this field are discussed.(c) 2022 Societe francaise de radiologie. Published by Elsevier Masson SAS. All rights reserved.

Automated summary

Artificial intelligence (AI) has been increasingly studied in musculoskeletal oncology imaging, with applications in primary and secondary bone tumors for tasks such as detection, segmentation, classification, and prognosis. However, further efforts are needed to improve AI reproducibility and achieve an acceptable level of evidence in clinical research. This review provides an overview of common AI techniques, including machine learning, deep learning, and radiomics, as well as recent developments and current results in musculoskeletal oncology. The limitations and future perspectives of AI in this field are also discussed.