Background Artificial-intelligence algorithms derive rules and patterns from large amounts of
data to calculate the probabilities of various outcomes using new sets of similar data. In …

The recent influx of machine learning centered investigations in the spine surgery literature
has led to increased enthusiasm as to the prospect of using artificial intelligence to create …

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 …

Earlier detection of individuals at the highest risk of developing diabetes is crucial to avoid
the disease's prevalence and progression. Therefore, we aim to build a data-driven …

Background Failure to achieve clinically significant outcome (CSO) improvement after total
hip arthroplasty (THA) imposes a potential cost-to-risk imbalance in the context of bundle …

Background and purpose—External validation of machine learning (ML) prediction models
is an essential step before clinical application. We assessed the proportion, performance …

Background Machine learning techniques can identify complex relationships in large
healthcare datasets and build prediction models that better inform physicians in ways that …

Background A preoperative estimation of survival is critical for deciding on the operative
management of metastatic bone disease of the extremities. Several tools have been …

Abstract Background Context In-hospital and short-term mortality in patients with spinal
epidural abscess (SEA) remains unacceptably high despite diagnostic and therapeutic …

Purpose To identify predictors of a true scaphoid fracture among patients with radial wrist
pain following acute trauma, train 5 machine learning (ML) algorithms in predicting scaphoid …