Automated identification of arthroplasty implants could aid in pre-operative planning and is a task which could be facilitated through artificial intelligence (AI) and deep learning. The purpose of this study was to develop and test the performance of a deep learning system (DLS) for automated identification and classification of knee arthroplasty (KA) on radiographs. We collected 237 AP knee radiographs with equal proportions of native knees, total KA (TKA), and unicompartmental KA (UKA), as well as 274 radiographs with equal proportions of Smith & Nephew Journey and Zimmer NexGen TKAs. Data augmentation was used to increase the number of images available for DLS development. These images were used to train, validate, and test deep convolutional neural networks (DCNN) to 1) detect the presence of TKA; 2) differentiate between TKA and UKA; and 3) differentiate between the 2 TKA models. Receiver operating characteristic (ROC) curves were generated with area under the curve (AUC) calculated to assess test performance.Introduction
Methods
Hip arthroplasty is one of the most common procedures performed every year however complications do occur. Prior studies have examined the impact of insurance status on complications after TJA in small or focused cohorts. The purpose of our study was to utilize a large all-payer inpatient healthcare database to evaluate the effect of patient insurance status on complications following hip arthroplasty. Data was obtained from the Nationwide Inpatient Sample between 2004 and 2011. Analysis included patients undergoing hip arthroplasty procedures determined by ICD-9 procedure codes. Patient demographics and comorbidities were analyzed and stratified by insurance type. The primary outcome was medical complications, surgical complications and mortality during the same hospitalization. A secondary analysis was performed using a matched cohort comparing patients with Medicare vs private insurance using the coarsened exact matching algorithm. Pearson's chi-squared test and multivariate regression were performed.Introduction
Methods
Shoulder arthroplasty (SA) is an effective procedure for managing patients with shoulder pain secondary to degenerative joint disease or end stage arthritis that has failed conservative treatment. Insurance status has been shown to be an indicator of patient morbidity and mortality. The objective of the current study is to evaluate the effect of patient insurance status on outcomes following shoulder replacement surgery. Data was obtained from the Nationwide Inpatient Sample between 2004 and 2011. Analysis included patients undergoing shoulder arthroplasty procedures determined by ICD-9 procedure codes. Patient demographics and comorbidities were analyzed and stratified by insurance type. The primary outcome was medical and surgical complications occurring during the same hospitalization with secondary analysis of mortality. Pearson's chi¬squared test and multivariate regression were performed.INTRODUCTION
METHODS
Knee arthroplasty is one of the most common inpatient surgeries procedures performed every year however complications do occur. Prior studies have examined the impact of insurance status on complications after TJA in small or focused cohorts. The purpose of our study was to utilize a large all-payer inpatient healthcare database to evaluate the effect of patient insurance status on complications following knee arthroplasty. Data was obtained from the Nationwide Inpatient Sample between 2004 and 2011. Analysis included patients undergoing knee arthroplasty procedures determined by ICD-9 procedure codes. Patient demographics and comorbidities were analyzed and stratified by insurance type. The primary outcome was medical complications, surgical complications and mortality during the same hospitalization. A secondary analysis was performed using a matched cohort comparing patients with Medicare vs private insurance using the coarsened exact matching algorithm. Pearson's chi-squared test and multivariate regression were performed.Introduction
Methods
