Deep learning applications in orthopaedics: a systematic review and future directions.

Abstract

INTRODUCTION: artificial intelligence and deep learning in orthopedics have gained mass interest in recent years. In prior studies, researchers have demonstrated different applications, from radiographic assessment to bone tumor diagnosis. The purpose of this review is to analyze the current literature on AI and deep learning tools to identify the most used tools in the risk assessment, outcome assessment, imaging, and basic science fields. MATERIAL AND METHODS: searches were conducted in PubMed, EMBASE and Google Scholar from January 2020 up to October 31st, 2023. We identified 862 studies, 595 of which were included in the systematic review. A total of 281 studies about radiographic assessment, 102 about spine-oriented surgery, 95 about outcome assessment, 84 about fundamental AI orthopedic education, and 33 basic science applications were included. Primary outcomes were diagnostic accuracy, study design and reporting standards reported in the literature. Estimates were pooled using random effects meta-analysis. RESULTS: 53 different imaging methods were used to measure radiographic aspects. A total of 185 different machine learning algorithms were used, with the convolutional neural network architecture being the most common (73%). To improve diagnostic accuracy and speed were the most commonly achieved results (62%). CONCLUSION: heterogeneity was high among the studies, and extensive variation in methodology, terminology and outcome measures was noted. This can lead to an overestimation of the diagnostic accuracy of DL algorithms for medical imaging. There is an immediate need for the development of artificial intelligence-specific guidelines to provide guidance around key issues in this field.

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