Outcomes Following Direct Anterior Approach Total Hip Arthroplasty: A Contemporary Multicenter Study.

Background: The direct anterior approach (DAA) is a popular approach for primary total hip arthroplasty (THA). However, the contemporary outcomes for DAA THA need further elucidation. Therefore, we aimed to describe implant survivorship, complications, and clinical outcomes after DAA THA.

Impact of Leg Position on Measurements Used to Detect Femoral Component Subsidence in THA.

Background: A fully automated artificial intelligence-based tool was developed to detect and quantify femoral component subsidence between serial radiographs. However, it did not account for measurement errors due to leg position differences, such as rotation or flexion, between comparative radiographs. If there are small differences in rotation or flexion of the leg between comparative radiographs, the impact on subsidence measurement is unclear.

Data-Driven Approach to Development of a Risk Score for Periprosthetic Joint Infections in Total Joint Arthroplasty Using Electronic Health Records.

Background: Periprosthetic joint infection (PJI) is an uncommon, but serious complication in total joint arthroplasty. Personalized risk prediction and risk factor management may allow better preoperative assessment and improved outcomes. We evaluated different data-driven approaches to develop surgery-specific PJI prediction models using large-scale data from the electronic health records (EHRs).

Uncertainty-Aware Deep Learning Characterization of Knee Radiographs for Large-Scale Registry Creation.

Background: We present an automated image ingestion pipeline for a knee radiography registry, integrating a multilabel image-semantic classifier with conformal prediction-based uncertainty quantification and an object detection model for knee hardware.

Methods: Annotators retrospectively classified 26,000 knee images detailing presence, laterality, prostheses, and radiographic views. They further annotated surgical construct locations in 11,841 knee radiographs.

Analyzing Racial Differences in Imaging Joint Replacement Registries Using Generative Artificial Intelligence: Advancing Orthopaedic Data Equity.

Background: Discrepancies in medical data sets can perpetuate bias, especially when training deep learning models, potentially leading to biased outcomes in clinical applications. Understanding these biases is crucial for the development of equitable healthcare technologies. This study employs generative deep learning technology to explore and understand radiographic differences based on race among patients undergoing total hip arthroplasty.

The Impact of Leg Length and Offset Change on Dislocation Risk Following Primary Total Hip Arthroplasty.

Background: Soft tissue management in total hip arthroplasty includes appropriate restoration and/or alteration of leg length (LL) and offset to re-establish natural hip biomechanics. The purpose of this study was to evaluate the effect of LL and offset-derived variables in a multivariable survival model for dislocation.

Methods: Clinical, surgical, and radiographic data was retrospectively acquired for 12,582 patients undergoing primary total hip arthroplasty at a single institution from 1998 to 2018.

Blood metal concentrations and cardiac structure and function in total joint arthroplasty patients.

Background: There is concern regarding potential long-term cardiotoxicity with systemic distribution of metals in total joint arthroplasty (TJA) patients.

Aim: To determine the association of commonly used implant metals with echocardiographic measures in TJA patients.

Methods: The study comprised 110 TJA patients who had a recent history of high chromium, cobalt or titanium concentrations.

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.

Deep learning classification of pediatric spinal radiographs for use in large scale imaging registries.

Purpose: The purpose of this study is to develop and apply an algorithm that automatically classifies spine radiographs of pediatric scoliosis patients.

Methods: Anterior-posterior (AP) and lateral spine radiographs were extracted from the institutional picture archive for patients with scoliosis. Overall, there were 7777 AP images and 5621 lateral images.

Synthetically enhanced: unveiling synthetic data's potential in medical imaging research.

Background: Chest X-rays (CXR) are essential for diagnosing a variety of conditions, but when used on new populations, model generalizability issues limit their efficacy. Generative AI, particularly denoising diffusion probabilistic models (DDPMs), offers a promising approach to generating synthetic images, enhancing dataset diversity. This study investigates the impact of synthetic data supplementation on the performance and generalizability of medical imaging research.

Surgical Approach and Body Mass Index Impact Risk of Wound Complications Following Total Hip Arthroplasty.

Background: Previous studies have suggested that wound complications may differ by surgical approach after total hip arthroplasty (THA), with particular attention toward the direct anterior approach (DAA). However, there is a paucity of data documenting wound complication rates by surgical approach and the impact of concomitant patient factors, namely body mass index (BMI). This investigation sought to determine the rates of wound complications by surgical approach and identify BMI thresholds that portend differential risk.

How are Oral Antibiotics Being Used in Total Joint Arthroplasty? A Review of the Literature.

While the role and benefit of perioperative intravenous (IV) antibiotics in patients undergoing total joint arthroplasty (TJA) is well-established, oral antibiotic use in TJA remains a controversial topic with wide variations in practice patterns. With this review, we aimed to better educate the orthopedic surgeon on when and how oral antibiotics may be used most effectively in TJA patients, and to identify gaps in the literature that could be clarified with targeted research. Extended oral antibiotic prophylaxis (EOAP) use in high-risk primary, aseptic revision, and exchange TJA for infection may be useful in decreasing periprosthetic joint infection (PJI) rates.

Extended Trochanteric Osteotomy in Revision Total Hip Arthroplasty.

Background: Removal of well-fixed femoral components during revision total hip arthroplasty (THA) can be difficult and time-consuming, leading to numerous complications, such as femoral perforation, bone loss, and fracture. Extended trochanteric osteotomies (ETOs), which provide wide exposure and direct access to the femoral canal under controlled conditions, have become a popular method to circumvent these challenges. ETOs were popularized by Wagner (i.

Artificial intelligence-based three-dimensional templating for total joint arthroplasty planning: a scoping review.

Purpose: The purpose of this review is to evaluate the current status of research on the application of artificial intelligence (AI)-based three-dimensional (3D) templating in preoperative planning of total joint arthroplasty.

Methods: This scoping review followed the PRISMA, PRISMA-ScR guidelines, and five stage methodological framework for scoping reviews. Studies of patients undergoing primary or revision joint arthroplasty surgery that utilised AI-based 3D templating for surgical planning were included.

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.

Whole Blood Metal Levels in the Setting of an Oncologic Endoprosthesis: Is There Cause for Concern?

Background: Massive modular endoprostheses have become a primary means of reconstruction after oncologic resection of a lower extremity tumor. These implants are commonly made with cobalt-chromium alloys that can undergo wear and corrosion, releasing cobalt and chromium ions into the surrounding tissue and blood. However, there are few studies about the blood metal levels in these patients.

The use of deep learning in medical imaging to improve spine care: A scoping review of current literature and clinical applications.

Background: Artificial intelligence is a revolutionary technology that promises to assist clinicians in improving patient care. In radiology, deep learning (DL) is widely used in clinical decision aids due to its ability to analyze complex patterns and images. It allows for rapid, enhanced data, and imaging analysis, from diagnosis to outcome prediction.

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.