Does maintaining external fixators during definitive fixation of bicondylar tibial plateau fractures influence fracture alignment and deep infection rates?

Introduction: External fixators are utilized to temporarily stabilize bicondylar tibial plateau fractures. They can be prepped during definitive surgery to help maintain fracture length and alignment. However, there is a potential for increased infection by leaving the external fixator on during the surgery.

Identifying Racial Disparities in Utilization and Clinical Outcomes of Ambulatory Hip Arthroscopy: Analysis of Temporal Trends and Causal Inference via Machine Learning.

Background: Arthroscopic diagnosis and treatment of femoroacetabular pathology has experienced significant growth in the last 30 years; nevertheless, reduced utilization of orthopaedic procedures has been observed among the underrepresented population.

Purpose/hypothesis: The purpose of this study was to examine racial differences in case incidence rates, outcomes, and complications in patients undergoing hip arthroscopy. It was hypothesized that racial and ethnic minority patients would undergo hip arthroscopy at a decreased rate compared with their White counterparts but that there would be no differences in clinical outcomes.

Management of Index Finger Metacarpophalangeal Joint Arthritis.

Metacarpophalangeal joint arthritis of the index finger is a debilitating disease often caused by osteoarthritis or inflammatory arthritides such as rheumatoid arthritis. Treatment options include nonsurgical management with nonsteroidal anti-inflammatory drugs, splinting, occupational therapy, corticosteroid injections, and disease-modifying antirheumatic drugs. Operative management options include arthrodesis and arthroplasty, which can be further broken down into silicone implants and 2 component resurfacing implants.

Overview of Artificial Intelligence Research Within Hip and Knee Arthroplasty.

Hip and knee arthroplasty are high-volume procedures undergoing rapid growth. The large volume of procedures generates a vast amount of data available for next-generation analytics. Techniques in the field of artificial intelligence (AI) can assist in large-scale pattern recognition and lead to clinical insights.

Anonymizing Radiographs Using an Object Detection Deep Learning Algorithm.

Radiographic markers contain protected health information that must be removed before public release. This work presents a deep learning algorithm that localizes radiographic markers and selectively removes them to enable de-identified data sharing. The authors annotated 2000 hip and pelvic radiographs to train an object detection computer vision model.

A Stepwise Approach to Analyzing Musculoskeletal Imaging Data With Artificial Intelligence.

The digitization of medical records and expanding electronic health records has created an era of "Big Data" with an abundance of available information ranging from clinical notes to imaging studies. In the field of rheumatology, medical imaging is used to guide both diagnosis and treatment of a wide variety of rheumatic conditions. Although there is an abundance of data to analyze, traditional methods of image analysis are human resource intensive.

Few-shot biomedical image segmentation using diffusion models: Beyond image generation.

Background: Medical image analysis pipelines often involve segmentation, which requires a large amount of annotated training data, which is time-consuming and costly. To address this issue, we proposed leveraging generative models to achieve few-shot image segmentation.

Methods: We trained a denoising diffusion probabilistic model (DDPM) on 480,407 pelvis radiographs to generate 256 ✕ 256 px synthetic images.

THA-AID: Deep Learning Tool for Total Hip Arthroplasty Automatic Implant Detection With Uncertainty and Outlier Quantification.

Background: Revision total hip arthroplasty (THA) requires preoperatively identifying in situ implants, a time-consuming and sometimes unachievable task. Although deep learning (DL) tools have been attempted to automate this process, existing approaches are limited by classifying few femoral and zero acetabular components, only classify on anterior-posterior (AP) radiographs, and do not report prediction uncertainty or flag outlier data.

Methods: This study introduces Total Hip Arhtroplasty Automated Implant Detector (THA-AID), a DL tool trained on 241,419 radiographs that identifies common designs of 20 femoral and 8 acetabular components from AP, lateral, or oblique views and reports prediction uncertainty using conformal prediction and outlier detection using a custom framework.

Educational Overview of the Concept and Application of Computer Vision in Arthroplasty.

Image data has grown exponentially as systems have increased their ability to collect and store it. Unfortunately, there are limits to human resources both in time and knowledge to fully interpret and manage that data. Computer Vision (CV) has grown in popularity as a discipline for better understanding visual data.

THA-Net: A Deep Learning Solution for Next-Generation Templating and Patient-specific Surgical Execution.

Background: This study introduces THA-Net, a deep learning inpainting algorithm for simulating postoperative total hip arthroplasty (THA) radiographs from a single preoperative pelvis radiograph input, while being able to generate predictions either unconditionally (algorithm chooses implants) or conditionally (surgeon chooses implants).

Methods: The THA-Net is a deep learning algorithm which receives an input preoperative radiograph and subsequently replaces the target hip joint with THA implants to generate a synthetic yet realistic postoperative radiograph. We trained THA-Net on 356,305 pairs of radiographs from 14,357 patients from a single institution's total joint registry and evaluated the validity (quality of surgical execution) and realism (ability to differentiate real and synthetic radiographs) of its outputs against both human-based and software-based criteria.

Application of Natural Language Processing in Total Joint Arthroplasty: Opportunities and Challenges.

Total joint arthroplasty is becoming one of the most common surgeries within the United States, creating an abundance of analyzable data to improve patient experience and outcomes. Unfortunately, a large majority of this data is concealed in electronic health records only accessible by manual extraction, which takes extensive time and resources. Natural language processing (NLP), a field within artificial intelligence, may offer a viable alternative to manual extraction.

An Overview of Machine Learning in Orthopedic Surgery: An Educational Paper.

The growth of artificial intelligence combined with the collection and storage of large amounts of data in the electronic medical record collection has created an opportunity for orthopedic research and translation into the clinical environment. Machine learning (ML) is a type of artificial intelligence tool well suited for processing the large amount of available data. Specific areas of ML frequently used by orthopedic surgeons performing total joint arthroplasty include tabular data analysis (spreadsheets), medical imaging processing, and natural language processing (extracting concepts from text).

Demystifying Statistics and Machine Learning in Analysis of Structured Tabular Data.

Electronic health records have facilitated the extraction and analysis of a vast amount of data with many variables for clinical care and research. Conventional regression-based statistical methods may not capture all the complexities in high-dimensional data analysis. Therefore, researchers are increasingly using machine learning (ML)-based methods to better handle these more challenging datasets for the discovery of hidden patterns in patients' data and for classification and predictive purposes.

Defining Clinically Meaningful Subgroups for Risk Stratification in Patients Undergoing Revision Total Hip Arthroplasty: A Combined Unsupervised and Supervised Machine Learning Approach.

Background: Studies developing predictive models from large datasets to risk-stratify patients under going revision total hip arthroplasties (rTHAs) are limited. We used machine learning (ML) to stratify patients undergoing rTHA into risk-based subgroups.

Methods: We retrospectively identified 7,425 patients who underwent rTHA from a national database.

A Deep Learning Tool for Automated Landmark Annotation on Hip and Pelvis Radiographs.

Background: Automatic methods for labeling and segmenting pelvis structures can improve the efficiency of clinical and research workflows and reduce the variability introduced with manual labeling. The purpose of this study was to develop a single deep learning model to annotate certain anatomical structures and landmarks on antero-posterior (AP) pelvis radiographs.

Methods: A total of 1,100 AP pelvis radiographs were manually annotated by 3 reviewers.

Fracture Gap Closure and Reduction Are Affected by the Orientation of the Headless Compression Screw.

Background: We evaluated the impact of a variable-pitch headless screw's angle of insertion relative to the fracture plane on fracture gap closure and reduction.

Methods: Variable-pitch, fully threaded headless screws were inserted into polyurethane blocks of "normal" bone model density using a custom jig. Separate trials were completed with a 28-mm screw placed perpendicular and oblique/longitudinal to varying fracture planes (0°, 15°, 30°, 45°, and 60°).

Creating High Fidelity Synthetic Pelvis Radiographs Using Generative Adversarial Networks: Unlocking the Potential of Deep Learning Models Without Patient Privacy Concerns.

Background: In this work, we applied and validated an artificial intelligence technique known as generative adversarial networks (GANs) to create large volumes of high-fidelity synthetic anteroposterior (AP) pelvis radiographs that can enable deep learning (DL)-based image analyses, while ensuring patient privacy.

Methods: AP pelvis radiographs with native hips were gathered from an institutional registry between 1998 and 2018. The data was used to train a model to create 512 × 512 pixel synthetic AP pelvis images.

The Effect of Derotational Kirschner Wires on Fracture Gap Reduction With Variable-Pitch Headless Screws.

Purpose: We evaluated the impact of angled derotational Kirschner wires (K-wires) on fracture gap reduction with variable-pitch headless screws.

Methods: Fully threaded variable-pitch headless screws (20 and 28 mm) were inserted into "normal" bone models of polyurethane blocks. In separate trials, derotational K-wires were inserted at predetermined angles of 0°, 15°, 30°, and 40° and compared with each other, with no K-wire as a control.

Assessment for Septic Arthritis in Immunocompetent and Immunocompromised Patients: A Single-Institution Study.

Introduction: Prompt diagnosis of septic arthritis is imperative to prevent irreversible joint damage. Immunocompromised patients are at an increased risk of septic arthritis as well as secondary systemic infection. Our aims were to identify features predictive of septic arthritis and to determine whether these features differed between immunocompetent and immunocompromised patients.