A Randomized Clinical Trial of Direct Anterior versus Mini-Posterior Total Hip Arthroplasty: Small, Early Functional Differences Did Not Lead to Meaningful Clinical Differences at 7.5 Years.

INTRODUCTION: Our previously reported randomized clinical trial of direct anterior approach (DAA) versus mini-posterior approach (MPA) total hip arthroplasty (THA) showed slightly faster initial recovery for patients who had a DAA and no differences in complications or clinical or radiographic outcomes beyond 8 weeks. The aims of the current study were to determine if early advantages of DAA led to meaningful clinical differences beyond 5 years and to identify differences in midterm complications. METHODS: Of 101 original patients, 93 were eligible for follow-up at a mean 7.5 years (range, 2.1 to 10). Clinical outcomes were compared with Harris Hip, 12-Item Short Form Health Survey (SF-12), and Hip Disability and Osteoarthritis Outcomes Scores (HOOS) scores and sub-scores, complications, reoperations, and revisions. RESULTS: Harris Hip scores were similar (95.3 ± 6.0 versus 93.5 ± 10.3 for DAA and MPA, respectively, P = 0.79). The SF-12 physical and mental scores were similar (46.2 ± 9.3 versus 46.2 ± 10.6, P = 0.79, and 52.3 ± 7.1 versus 55.2 ± 4.5, P = 0.07 in the DAA and MPA groups, respectively). The HOOS scores were similar (97.4 ± 7.9 versus 96.3 ± 6.7 for DAA and MPA, respectively, P = 0.07). The HOOS quality of life subscores were 96.9 ± 10.8 versus 92.3 ± 16.0 for DAA and MPA, respectively (P = 0.046). No clinical outcome met the minimally clinically important difference. There were 4 surgical complications in the DAA group (1 femoral loosening requiring revision, 1 dislocation treated closed, and 2 wound dehiscences requiring debridement), and 6 surgical complications in the MPA group (3 dislocations, 2 treated closed, and 1 revised to dual-mobility; 2 intraoperative fractures treated with a cable; and 1 wound dehiscence treated nonoperatively). CONCLUSIONS: At a mean of 7.5 years, this RCT demonstrated no clinically meaningful differences in outcomes, complications, reoperations, or revisions between DAA and MPA THA.

Incidence, treatment, and outcomes of modern dual-mobility intraprosthetic dissociations.

Aims: Dual-mobility (DM) components are increasingly used to prevent and treat dislocation after total hip arthroplasty (THA). Intraprosthetic dissociation (IPD) is a rare complication of DM that is believed to have decreased with contemporary implants. This study aimed to report incidence, treatment, and outcomes of contemporary DM IPD. Methods: A total of 1,453 DM components were implanted at a single academic institution between January 2010 and December 2021: 695 in primary and 758 in revision THA. Of these, 49 presented with a dislocation of the large DM head and five presented with an IPD. At the time of closed reduction of the large DM dislocation, six additional IPDs occurred. The mean age was 64 years (SD 9.6), 54.5% were female (n = 6), and mean follow-up was 4.2 years (SD 1.8). Of the 11 IPDs, seven had a history of instability, five had abductor insufficiency, four had prior lumbar fusion, and two were conversions for failed fracture management. Results: The incidence of IPD was 0.76%. Of the 11 IPDs, ten were missed either at presentation or after attempted reduction. All ten patients with a missed IPD were discharged with a presumed reduction. The mean time from IPD to surgical treatment was three weeks (0 to 23). One patient died after IPD prior to revision. Of the ten remaining hips with IPD, the DM head was exchanged in two, four underwent acetabular revision with DM exchange, and four were revised to a constrained liner. Of these, five (50%) underwent reoperation at a mean 1.8 years (SD 0.73), including one additional acetabular revision. No patients who underwent initial acetabular revision for IPD treatment required subsequent reoperation. Conclusion: The overall rate of IPD was low at 0.76%. It is essential to identify an IPD on radiographs as the majority were missed at presentation or after iatrogenic dissociation. Surgeons should consider acetabular revision for IPD to allow conversion to a larger DM head, and take care to remove impinging structures that may increase the risk of subsequent failure.

The Frank Stinchfield Award: Assembly and Dissociation Forces Differ Between Commonly Used Dual Mobility Implants: A Biomechanical Study.

BACKGROUND: Intraprosthetic dissociation (IPD) is a complication unique to dual mobility (DM) implants where the outer polyethylene head dissociates from the inner femoral head. Increasing reports of IPD at the time of closed reduction of large head DM dislocations prompted this biomechanical study evaluating the assembly and dissociation forces of DM heads. METHODS: We tested 17 polyethylene DM heads from 5 vendors. Of the heads, 12 were highly cross-linked polyethylene (4 vendors) and 5 were infused with vitamin E (2 vendors). Heads were between 46 and 47 mm in diameter, accepting a 28 mm-inner ceramic head. Implants were assembled and disassembled using a servohydraulic machine that recorded the forces and torques applied during testing. Dissociation was tested via both axial pull-out and lever-out techniques, where lever-out simulated stem-on-acetabular component impingement. RESULTS: The initial maximum assembly force was significantly different between all vendors (P < .01) and decreased for all implants with subsequent assembly. Vendor 4-E (Link with vitamin E) heads required the highest assembly force (1,831.9 ± 81.95 N), followed by Vendor 3 (Smith & Nephew), Vendor 5 (DePuy Synthes), Vendor 1-E (Zimmer Biomet with vitamin E), Vendor 2 (Stryker), and Vendor 1 (Zimmer Biomet Arcom). Vendor 4-E implants showed the greatest dissociation resistance in both pull-out (2,059.89 N, n = 1) and lever-out (38.95 ± 2.79 Nm) tests. Vendor 1-E implants with vitamin E required higher assembly force, dissociation force, and energy than Vendor 1 heads without vitamin E. CONCLUSIONS: There were notable differences in DM assembly and dissociation forces between implants. Diminishing force was required for assembly with each additional trial across vendors. Vendor 4-E DM heads required the highest assembly and dissociation forces. Vitamin E appeared to increase the assembly and dissociation forces. Based on these results, DM polyethylene heads should not be reimplanted after dissociation, and there may be a role for establishing a minimum dissociation energy standard to minimize IPD risk.

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. METHODS: This multicenter study retrospectively evaluated all primary THA patients from 2010 to 2023. Patients were classified by skin incision as having a laterally based approach (posterior or lateral approach) or DAA (longitudinal incision). We identified 17,111 patients who had 11,585 laterally based (68%) and 5,526 (32%) DAA THAs. The mean age was 65 years (range, 18 to 100), 8,945 patients (52%) were women, and the mean BMI was 30 (range, 14 to 79). Logistic regression and cut-point analyses were performed to identify an optimal BMI cutoff, overall and by approach, with respect to the risk of wound complications at 90 days. RESULTS: The 90-day risk of wound complications was higher in the DAA group versus the laterally based group, with an absolute risk of 3.6% versus 2.6% and a multivariable adjusted odds ratio of 1.5 (P < .001). Cut-point analyses demonstrated that the risk of wound complications increased steadily for both approaches, but most markedly above a BMI of 33. CONCLUSIONS: Wound complications were higher after longitudinal incision DAA THA compared to laterally based approaches, with a 1% higher absolute risk and an adjusted odds ratio of 1.5. Furthermore, BMI was an independent risk factor for wound complications regardless of surgical approach, with an optimal cut-point BMI of 33 for both approaches. These data can be used by surgeons to help consider the risks and benefits of approach selection. LEVEL OF EVIDENCE: Level III.

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. Outcome measures included dataset and model development characteristics, AI performance metrics, and time performance. After AI-based 3D planning, the accuracy of component size and placement estimation and postoperative outcome data were collected. RESULTS: Nine studies satisfied inclusion criteria including a focus on computed tomography (CT) or magnetic resonance imaging (MRI)-based AI templating for use in hip or knee arthroplasty. AI-based 3D templating systems reduced surgical planning time and improved implant size/position and imaging feature estimation compared to conventional radiographic templating. Several components of data processing and model development and testing were insufficiently covered in the studies included in this scoping review. CONCLUSIONS: AI-based 3D templating systems have the potential to improve preoperative planning for joint arthroplasty surgery. This technology offers more accurate and personalized preoperative planning, which has potential to improve functional outcomes for patients. However, deficiencies in several key areas, including data handling, model development, and testing, can potentially hinder the reproducibility and reliability of the methods proposed. As such, further research is needed to definitively evaluate the efficacy and feasibility of these systems.

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. QUESTION/PURPOSE: What is the whole blood cobalt and chromium ion level in patients with massive modular endoprostheses? METHODS: We performed a cross-sectional study of our total joints registry to identify patients with a history of an endoprosthetic reconstruction performed at our institution. Patients who were alive at the time of our review in addition to those undergoing an endoprosthetic reconstruction after an oncologic resection were included. Whole blood samples were obtained from 27 (14 male and 13 female) patients with a history of a lower extremity oncologic endoprosthesis. The median time from surgery to blood collection was 8 years (range 6 months to 32 years). Blood samples were collected and stored in metal-free ethylenediaminetetraacetic acid tubes. Samples were analyzed on an inductively coupled plasma mass spectrometer in an International Organization for Standardization seven-class clean room using polytetrafluoroethylene-coated instruments to reduce the risk of metal contamination. The analytical measuring range was 1 to 200 ng/mL for chromium and cobalt. Cobalt and chromium levels were considered elevated when the blood level was ≥ 1 ppb. RESULTS: Cobalt levels were elevated in 59% (16 of 27) of patients, and chromium levels were elevated in 26% (seven of 27). In patients with elevated metal ion values, 15 of 17 patients had a reconstruction using a Stryker/Howmedica Global Modular Replacement System implant. CONCLUSION: Blood metal levels were elevated in patients who received reconstructions using modular oncology endoprostheses Future work is needed to establish appropriate follow-up routines and determine whether and when systemic complications occur because of elevated metal levels and how to potentially address these elevated levels when complications occur. Prospective and retrospective collaboration between multiple centers and specialty societies will be necessary to address these unknown questions in this potentially vulnerable patient group. LEVEL OF EVIDENCE: Level IV, therapeutic study.

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. RESULTS: The surgical validity of synthetic postoperative radiographs was significantly higher than their real counterparts (mean difference: 0.8 to 1.1 points on 10-point Likert scale, P < .001), but they were not able to be differentiated in terms of realism in blinded expert review. Synthetic images showed excellent validity and realism when analyzed with already validated deep learning models. CONCLUSION: We developed a THA next-generation templating tool that can generate synthetic radiographs graded higher on ultimate surgical execution than real radiographs from training data. Further refinement of this tool may potentiate patient-specific surgical planning and enable technologies such as robotics, navigation, and augmented reality (an online demo of THA-Net is available at: https://demo.osail.ai/tha_net).

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. We evaluated THA-AID using internal, external, and out-of-domain test sets and compared its performance with human experts. RESULTS: THA-AID achieved internal test set accuracies of 98.9% for both femoral and acetabular components with no significant differences based on radiographic view. The femoral classifier also achieved 97.0% accuracy on the external test set. Adding conformal prediction increased true label prediction by 0.1% for acetabular and 0.7 to 0.9% for femoral components. More than 99% of out-of-domain and >89% of in-domain outlier data were correctly identified by THA-AID. CONCLUSIONS: The THA-AID is an automated tool for implant identification from radiographs with exceptional performance on internal and external test sets and no decrement in performance based on radiographic view. Importantly, this is the first study in orthopedics to our knowledge including uncertainty quantification and outlier detection of a DL model.

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. Data were split into training, validation, and test sets at the patient level. Extracted markers were then characterized using an image processing algorithm, and potentially useful markers (eg, "L" and "R") without identifying information were retained. The model achieved an area under the precision-recall curve of 0.96 on the internal test set. The de-identification accuracy was 100% (400 of 400), with a de-identification false-positive rate of 1% (eight of 632) and a retention accuracy of 93% (359 of 386) for laterality markers. The algorithm was further validated on an external dataset of chest radiographs, achieving a de-identification accuracy of 96% (221 of 231). After fine-tuning the model on 20 images from the external dataset to investigate the potential for improvement, a 99.6% (230 of 231, P = .04) de-identification accuracy and decreased false-positive rate of 5% (26 of 512) were achieved. These results demonstrate the effectiveness of a two-pass approach in image de-identification. Keywords: Conventional Radiography, Skeletal-Axial, Thorax, Experimental Investigations, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Chang and Li in this issue.

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 âœ• 256 px synthetic images. The DDPM was conditioned on demographic and radiologic characteristics and was rigorously validated by domain experts and objective image quality metrics (Frechet inception distance [FID] and inception score [IS]). For the next step, three landmarks (greater trochanter [GT], lesser trochanter [LT], and obturator foramen [OF]) were annotated on 45 real-patient radiographs; 25 for training and 20 for testing. To extract features, each image was passed through the pre-trained DDPM at three timesteps and for each pass, features from specific blocks were extracted. The features were concatenated with the real image to form an image with 4225 channels. The feature-set was broken into random patches, which were fed to a U-Net. Dice Similarity Coefficient (DSC) was used to compare the performance with a vanilla U-Net trained on radiographs. RESULTS: Expert accuracy was 57.5 % in determining real versus generated images, while the model reached an FID = 7.2 and IS = 210. The segmentation UNet trained on the 20 feature-sets achieved a DSC of 0.90, 0.84, and 0.61 for OF, GT, and LT segmentation, respectively, which was at least 0.30 points higher than the naively trained model. CONCLUSION: We demonstrated the applicability of DDPMs as feature extractors, facilitating medical image segmentation with few annotated samples.

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. This article describes commonly used ML methods in structured data analysis with examples in orthopedic surgery. We present practical considerations in starting an ML project and appraising published studies in this field.

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). Previous studies have discussed models able to identify fractures in radiographs, identify implant type in radiographs, and determine the stage of osteoarthritis based on walking analysis. Despite the growing popularity of ML, there are limitations including its reliance on "good" data, potential for overfitting, long life cycle for creation, and ability to only perform one narrow task. This educational article will further discuss a general overview of ML, discussing these challenges and including examples of successfully published models.

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. Using NLP, a researcher can analyze written and spoken data and extract data in an organized manner suitable for future research and clinical use. This article will first discuss common subtasks involved in an NLP pipeline, including data preparation, modeling, analysis, and external validation, followed by examples of NLP projects. Challenges and limitations of NLP will be discussed, closing with future directions of NLP projects, including large language models.