Machine learning in orthopaedic surgery

Abstract

Artificial intelligence and machine learning in orthopaedic surgery has gained mass interest over the last decade or so. In prior studies, researchers have demonstrated that machine learning in orthopaedics can be used for different applications such as fracture detection, bone tumor diagnosis, detecting hip implant mechanical loosening, and grading osteoarthritis. As time goes on, the utility of artificial intelligence and machine learning algorithms, such as deep learning, continues to grow and expand in orthopaedic surgery. The purpose of this review is to provide an understanding of the concepts of machine learning and a background of current and future orthopaedic applications of machine learning in risk assessment, outcomes assessment, imaging, and basic science fields. In most cases, machine learning has proven to be just as effective, if not more effective, than prior methods such as logistic regression in assessment and prediction. With the help of deep learning algorithms, such as artificial neural networks and convolutional neural networks, artificial intelligence in orthopaedics has been able to improve diagnostic accuracy and speed, flag the most critical and urgent patients for immediate attention, reduce the amount of human error, reduce the strain on medical professionals, and improve care. Because machine learning has shown diagnostic and prognostic uses in orthopaedic surgery, physicians should continue to research these techniques and be trained to use these methods effectively in order to improve orthopaedic treatment.

Keywords: Artificial intelligence, Machine learning, Supervised learning, Unsupervised learning, Deep learning, Orthopaedic surgery

Core Tip: With the mass interest artificial intelligence and machine learning have garnered in orthopaedic surgery, a literature review of recent studies is necessary. By demonstrating the utility of various machine learning algorithms across various subspecialties of orthopaedic surgery, researchers should encourage physicians to understand the benefits of machine learning techniques and learn how to effectively incorporate these elements into their own practice to improve patient care. This clinical review outlines the concepts of machine learning and summarizes current and future orthopaedic applications of machine learning in risk assessment, outcomes assessment, imaging, and basic science fields.