Du Hyun Ro

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. METHODS: This study retrospectively reviewed 1686 patients who un

INTRODUCTION: Knee osteoarthritis (OA) can affect the hip and ankle joints, as these three joints operate as a kinetic/kinematic chain while walking. PURPOSE: This study was performed to compare (1) hip and ankle joint gait mechanics between knee OA and control groups and (2) to investigate the effects of knee gait mechanics on the ipsilateral hip and ankle joint. METHODS: The study group included 89 patients with end-stage knee OA and 42 age- and sex-matched controls without knee pain or OA. Ki

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. The model was developed using data on 5302 patients from two hospitals and externally validated in 455 patients from the thi

PURPOSE: This study hypothesized that the use of bisphosphonates (BPs) after total joint arthroplasty (TJA) is associated with a lower implant revision rate. This study aimed (1) to investigate the association between BP use and the revision rate of TJA and (2) to determine the relationship between the medication period and the revision rate of TJA. METHODS: National Health Insurance Service data on surgeries, medications, diagnoses, and screenings of 50 million Koreans were reviewed. People who

Knee osteoarthritis (KOA) is characterized by pain and decreased gait function. We aimed to find KOA-related gait features based on patient reported outcome measures (PROMs) and develop regression models using machine learning algorithms to estimate KOA severity. The study included 375 volunteers with variable KOA grades. The severity of KOA was determined using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). WOMAC scores were used to classify disease severity into th

This study investigated sex differences in knee biomechanics and investigated determinants for difference in a geriatric population. Age-matched healthy volunteers (42 males and 42 females, average age 65 years) without knee OA were included in the study. Subjects underwent physical examination on their knee and standing full-limb radiography for anthropometric measurements. Linear, kinetic, and kinematic parameters were compared using a three-dimensional, 12-camera motion capture system. Gait p

PURPOSE: We evaluated the sensitivity and specificity of ankle-brachial index (ABI), photoplethysmography (PPG), and continuous-wave Doppler ultrasound (CWD) in the detection of anatomically stenotic peripheral arterial disease (PAD). METHODS: Ninety-seven patients (194 legs) patients who had coincidentally undergone computed tomography angiography (CTA), ABI, PPG, and CWD for the evaluation of PAD were retrospectively reviewed. Sensitivity and specificity were measured. RESULTS: Among 194 legs,

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. To train the data, 15 anatomical landmarks were marked by two orthopaedic

Retrospective cohort study, Level III.

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. The model was developed and trained with 1,931 patients from one hospital and externally validated with 2,049 patients

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 orthopedi

BACKGROUND: Achieving consistent accuracy in radiographic measurements across different equipment and protocols is challenging. This study evaluates an advanced deep learning (DL) model, building upon a precursor, for its proficiency in generating uniform and precise alignment measurements in full-leg radiographs irrespective of institutional imaging differences. METHODS: The enhanced DL model was trained on over 10,000 radiographs. Utilizing a segmented approach, it separately identified and ev

Abstract Background Sarcopenia, an age-related loss of skeletal muscle mass and function, is correlated with adverse outcomes after some surgeries. This study examined the characteristics of sarcopenic patients undergoing primary total knee arthroplasty (TKA), and identified low muscle mass as an independent risk factor for postoperative TKA complications. Methods A retrospective cohort study examined 452 patients who underwent TKA. The skeletal muscle index (SMI) was obtained via bioelectrical