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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.

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Background: Return to sport (RTS) after meniscectomy is an important metric for young, active patients. However, the impact of the duration from surgery to RTS on clinical outcomes is not fully understood and is not reflected in outcome scores. Purpose: To establish when patients RTS after meniscectomy and to determine predictive measures for the ability to return to their preinjury activity.

Study Design. A systematic review. Objective. The aim of this study was to determine the association between study outcomes and the presence of a conflict of interest (COI) in the lumbar disc arthroplasty (LDA) literature. Summary of Background Data. Previous studies have evaluated the efficacy of LDA as a surgical alternative to arthrodesis. As investigators may have financial relationships with LDA device companies, it is important to consider the role of COI on study outcomes. Methods. A systematic review was performed to identify articles reporting clinical outcomes of LDA. Any financial COIs disclosed were recorded and confirmed through Open Payments and ProPublica databases. Study outcomes were graded as favorable, unfavorable, or equivocal. Pearson Chi-squared analysis was used to determine an association between COI and study outcomes. Favorable outcomes were tested for an association with study characteristics using Poisson regression with robust error variance. Results. Fifty-seven articles were included, 30 had a financial COI, while 27 did not. Ninety percent of the conflicted studies disclosed their COI in the article. Studies with United States authors were more likely to be conflicted (P = 0.019). A majority of studies reported favorable outcomes for LDA (n = 39). Conflicted studies were more likely to report favorable outcomes than nonconflicted studies (P = 0.020). Articles with COIs related to consultant fees (P = 0.003), research funding (P = 0.002), and stock ownership (P < 0.001) were more likely to report favorable outcomes. Conclusion. This study highlights the importance for authors to accurately report conflicting relationships with industry. As such, orthopedic surgeons should critically evaluate study outcomes with regard to potential conflicts before recommending LDA as a surgical option to their patients. Level of Evidence: 3

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