Artificial intelligence in orthopedic surgery

Abstract

Artificial intelligence (AI), first proposed by Prof. John McCarthy in 1956, aims to reproduce human intelligence using computers. Machine learning (ML) is a form of AI that uses computational algorithms that learn and improve with experience.[1] The two main forms of ML are supervised and unsupervised. In supervised ML, algorithms are given labeled data, which is used to predict disease outcomes in a new patient. In contrast, unsupervised ML is used to identify patterns without training; the algorithm learns the inherent structure of the data by searching for common characteristics.[1] AI has gained tremendous popularity in recent years, and some AI techniques such as search engines, voice recognition software, and autonomous driving vehicles are now part of our daily lives. AI research is also being conducted in many medical fields, and shows great promise in promoting practice efficacy, personalizing patient management, and improving research capacity.[2,3] We aimed to outline the current applications and the future perspective of AI in orthopedics. AI techniques have made great improvements in every step of the medical imaging pathway, from acquisition and reconstruction to analysis and interpretation.[4] By incorporating information from the patient's medical records (including symptoms, laboratory results, and physical examination findings), AI identifies the most appropriate patient-specific imaging examination and determines the most appropriate protocol.[5] AI can also potentially increase the speed of magnetic resonance imaging (MRI) data acquisition and decrease the computed tomography (CT) radiation dose.[6] The most popular area of AI research is image interpretation. Rather than replacement of the radiologist, the use of AI helps the radiologist to improve the diagnostic accuracy and prevent errors and observer fatigue. AI algorithms have been applied to various conditions, including the diagnosis of fractures, osteoarthritis, bone age, and bone strength.[4] AI performs as well as or better than orthopedic surgeons in detecting fractures of the proximal humerus, hand, wrist, ankle, and vertebral compression fractures on radiographs.[4,7] AI also has potential applications in the automatic detection of hip or knee osteoarthritis on radiography, with performance comparable to that of an attending radiologist.[8] AI can help automate the grading of lumbar disc pathology on MRI using various classification systems, with an accuracy of 95.6% for disc detection and labeling.[9] Furthermore, AI improves the accuracy of bone age interpretation compared with aging done by a radiologist alone; however, the most accurate values are achieved when AI is used in combination with a radiologist.[10] AI also improves quantitative image analysis by allowing automatic segmentation of the area of interest, and many studies have focused on knee cartilage segmentation, with promising initial results.[11] However, whilst AI-assisted image interpretation can be accurate, it does require large training datasets, which may be costly and attenuate service inequality. With ongoing technological advances, AI in imaging will improve and become more widely applied. Another major potential use for AI in healthcare is in predicting the clinical outcome of patients based on a clinical dataset, genomic information, and medical images. Risk assessment and outcome prediction have always been challenging in clinical medicine. AI offers a new direction that could potentially overcome these challenges. In orthopedics, ML can be used to guide the management of patients by providing a patient-specific predicted rate of post-operative complications following lumbar fusion surgery.[12] In addition, visual and inertial sensor data can be analyzed by ML to predict injury risk patterns associated with dynamic knee valgus.[3] The AI technique can help the doctor to make a diagnosis or decision. In the United States, the IBM Watson Health cognitive computing sy