Total Knee Arthroplasty: Is It Safe? A Single-Center Study of 4,124 Patients in South Korea.

Background: Although total knee arthroplasty (TKA) is considered an effective treatment for knee osteoarthritis, it carries risks of complications. With a growing number of TKAs performed on older patients, understanding the cause of mortality is crucial to enhance the safety of TKA. This study aimed to identify the major causes of short- and long-term mortality after TKA and report mortality trends for major causes of death.

Automated Detection of Surgical Implants on Plain Knee Radiographs Using a Deep Learning Algorithm.

: The number of patients who undergo multiple operations on a knee is increasing. The objective of this study was to develop a deep learning algorithm that could detect 17 different surgical implants on plain knee radiographs. : An internal dataset consisted of 5206 plain knee antero-posterior X-rays from a single, tertiary institute for model development.

Deep learning-based landmark recognition and angle measurement of full-leg plain radiographs can be adopted to assess lower extremity alignment.

Purpose: Evaluating lower extremity alignment using full-leg plain radiographs is an essential step in diagnosis and treatment of patients with knee osteoarthritis. The study objective was to present a deep learning-based anatomical landmark recognition and angle measurement model, using full-leg radiographs, and validate its performance.

Methods: A total of 11,212 full-leg plain radiographs were used to create the model.

A machine-learning model to predict postoperative delirium following knee arthroplasty using electronic health records.

Background: Postoperative delirium is a challenging complication due to its adverse outcome such as long hospital stay. The aims of this study were: 1) to identify preoperative risk factors of postoperative delirium following knee arthroplasty, and 2) to develop a machine-learning prediction model.

Method: A total of 3,980 patients from two hospitals were included in this study.

High-resolution knee plain radiography image synthesis using style generative adversarial network adaptive discriminator augmentation.

In this retrospective study, 10,000 anteroposterior (AP) radiography of the knee from a single institution was used to create medical data set that are more balanced and cheaper to create. Two types of convolutional networks were used, deep convolutional GAN (DCGAN) and Style GAN Adaptive Discriminator Augmentation (StyleGAN2-ADA). To verify the quality of generated images from StyleGAN2-ADA compared to real ones, the Visual Turing test was conducted by two computer vision experts, two orthopedic surgeons, and a musculoskeletal radiologist.

Automated Risk Stratification of Hip Osteoarthritis Development in Patients With Femoroacetabular Impingement Using an Unsupervised Clustering Algorithm: A Study From the Rochester Epidemiology Project.

Background: Studies evaluating the natural history of femoroacetabular impingement (FAI) are limited.

Purpose: To stratify the risk of progression to osteoarthritis (OA) in patients with FAI using an unsupervised machine-learning algorithm, compare the characteristics of each subgroup, and validate the reproducibility of staging.

Study Design: Cohort study (prognosis); Level of evidence, 2.

A web-based machine-learning algorithm predicting postoperative acute kidney injury after total knee arthroplasty.

Purpose: Acute kidney injury (AKI) is a deleterious complication after total knee arthroplasty (TKA). The purposes of this study were to identify preoperative risk factors and develop a web-based prediction model for postoperative AKI, and assess how AKI affected the progression to ESRD.

Method: The study included 5757 patients treated in three tertiary teaching hospitals.

Transfusion after total knee arthroplasty can be predicted using the machine learning algorithm.

Purpose: A blood transfusion after total knee arthroplasty (TKA) is associated with an increase in complication and infection rates. However, no studies have been conducted to predict transfusion after TKA using a machine learning algorithm. The purpose of this study was to identify informative preoperative variables to create a machine learning model, and to provide a web-based transfusion risk-assessment system for clinical use.