CORR Insights®: What Is the Accuracy of Three Different Machine Learning Techniques to Predict Clinical Outcomes After Shoulder Arthroplasty?

Machine learning is at the heart of a variety of applications in orthopaedic surgery. Guided by increasingly stringent statistical standards, such as the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis statement [2] and the Strengthening the Reporting of Observational studies in Epidemiology initiative, available at www.strobe-statement.org, investigators with good-quality clinical data can produce models trained to estimate the likelihood of any number of outcomes. In this issue ofClinical Orthopaedics and Related Research,Kumar et al. [3] conducted an experiment designed to determine whether three machinelearning techniques could predict surgical outcomes in patients undergoing total shoulder arthroplasty. Rather than designing classifiers, which estimate the likelihood of a particular event, they used regression, extreme gradient boosting, and a convolutional artificial neural network to generate discrete estimates of pain scores, ROM, and scores for a variety of measures, including the American Shoulder and Elbow Surgery, University of California Los Angeles, Constant, and global shoulder function scores. In an impressive application of this technology, the team was able to predict both shortand long-term outcomes 5 years postoperatively, while considering the minimal clinically important difference thresholds for each measure. Kumar et al. [3] applied what is called supervised machine learning. They allowed the algorithms to consider only specific preoperative data, then evaluated the output to ensure it made sense clinically. Their input data were meticulously collected from 1895 patients who underwent total shoulder arthroplasty by one of 30 surgeons using a single implant system. The algorithms learned patterns within the data that may not be readily apparent. In some cases, the patterns may be unique to the training data and may not be generalizable to a broader patient population. However, Kumar et al. [3] mitigated this risk by starting with data provided by a large group of surgeons. In addition, they validated the model’s performance on a subset of their data that had been sequestered and hidden from the training process. In doing so, they required each model to predict outcomes using data it had never before encountered. Using good-quality data and rigorous validation methods is a recipe for success, and increases the likelihood that the models developed in this experiment are generalizable to a wider patient population. This CORR Insights is a commentary on the article “What Is the Accuracy of Three Different Machine Learning Techniques to Predict Clinical Outcomes After Shoulder Arthroplasty? ” by Kumar and colleagues available at: DOI: 10.1097/CORR. 0000000000001263. The author certifies that he, or a member of his immediate family, has no funding or commercial associations (eg, consultancies, stock ownership, equity interest, patent/licensing arrangements, etc.) that might pose a conflict of interest in connection with the submitted article. The opinions expressed are those of the writer, and do not reflect the opinion or policy of CORR or The Association of Bone and Joint Surgeons. J. A. Forsberg MD, PhD (✉), Director of Orthopaedic Oncology, Walter Reed National Military Medical Center, Department of Orthopaedic Surgery, Bethesda, MD, 20889 USA, Email: jaforsberg@me.com