Bradley S. Schoch

Artificial intelligence (AI) is permeating many aspects of daily life. Medicine, and specifically orthopaedic surgery, is being influenced by the rapid growth and success of AI in serving as an adjunct to medical expertise. AI includes many nested subfields and applications such as computer vision, language models, and predictive analytics. Using standard machine learning methods, clinical decision support tools are being developed for clinicians to provide evidence-based, individualized care. Some models predict clinical and functional outcomes after a procedure, whereas others may incorporate administrative metrics to predict factors, such as length of stay, return to the emergency department, and complications. The application of AI to medical imaging focuses on the extraction of a large amount of quantitative data in the form of predefined features from clinical images that humans cannot perceive, a field known as radiomics. There has been recent interest, particularly in oncology, to apply radiomics to aid in diagnosis and prognostication of outcomes. Given the robust growth of radiomics literature, and the anticipated application in orthopaedic surgery, the purpose of this review article is to educate the orthopaedic surgery community on the practical importance and the basics of radiomics.

Background/Objectives: Non-typhoidal Salmonella (NTS) species are well-recognized causes of invasive infection in patients with sickle cell disease (SCD), with a particular predilection for the musculoskeletal system. Although Salmonella osteomyelitis is well described in this population, septic arthritis is uncommon, especially involving the shoulder joint. We describe a case of NTS bacteremia complicated by native shoulder septic arthritis in a patient with SCD and review its clinical implications. Methods: We report the clinical course, diagnostic evaluation, microbiologic findings, imaging studies, and management of a 22-year-old man with homozygous SCD who presented with a vaso-occlusive pain crisis and subsequently developed severe sepsis with persistent Salmonella enterica bacteremia following ingestion of undercooked poultry. Persistent bacteremia prompted further evaluation for metastatic infection using advanced imaging and diagnostic arthrocentesis. Results: Whole-body imaging identified septic arthritis of the native right shoulder, which was confirmed by synovial fluid cultures growing Salmonella species. The patient underwent arthroscopic irrigation and debridement for source control. Antimicrobial therapy was narrowed to intravenous ceftriaxone based on susceptibility data and continued for six weeks. The patient demonstrated clinical improvement with resolution of bacteremia and was discharged to rehabilitation to complete therapy. Conclusions: This case highlights the importance of a careful exposure history, including foodborne sources, in patients with SCD presenting with invasive Salmonella infection. Persistent bacteremia should prompt early investigation for metastatic foci, and timely surgical source control combined with targeted antimicrobial therapy is essential for optimal outcomes in this population.

While anatomic total shoulder arthroplasty is a successful procedure that provides reliable pain relief and restoration of function in most patients, its success has been limited by glenoid component loosening. While series reporting the outcomes of inlay glenoid components have demonstrated excellent clinical outcomes with low rates of component loosening and need for revision, surgeons have been hesitant to adopt these implants due to concerns of inadequate pain relief secondary to the remaining glenoid rim contacting the humeral head implant. The inset glenoid component, a variant of the traditional inlay components, has gained interest because its design aims to achieve similar stability to traditional inlay components through implantation within strong subchondral bone, reduce the amount of glenoid vault removed compared to inlay components, and has a glenoid face designed to limit the rocking-horse phenomenon. In limited series, the inset glenoid component has demonstrated superior biomechanical and clinical performance compared to traditional onlay glenoid components. Although there have been minimal clinical studies investigating the inset glenoid in comparison to onlay and inlay components to date, a subset of case series with short-term follow-up have demonstrated favorable outcomes. The purpose of this article was to review the design rationale, biomechanical evidence, and clinical performance of the inset glenoid component.

Introduction: We developed a computed tomography (CT)-based tool designed for automated segmentation of deltoid muscles, enabling quantification of radiomic features and muscle fatty infiltration. Prior to use in a clinical setting, this machine learning (ML)-based segmentation algorithm requires rigorous validation. The aim of this study is to conduct shoulder expert validation of a novel deltoid ML auto-segmentation and quantification tool. Materials and Methods: A SwinUnetR-based ML model trained on labeled CT scans is validated by three expert shoulder surgeons for 32 unique patients. The validation evaluates the quality of the auto-segmented deltoid images. Specifically, each of the three surgeons reviewed the auto-segmented masks relative to CT images, rated masks for clinical acceptance, and performed a correction on the ML-generated deltoid mask if the ML mask did not completely contain the full deltoid muscle, or if the ML mask included any tissue other than the deltoid. Non-inferiority of the ML model was assessed by comparing ML-generated to surgeon-corrected deltoid masks versus the inter-surgeon variation in metrics, such as volume and fatty infiltration. Results: The results of our expert shoulder surgeon validation demonstrates that 97% of ML-generated deltoid masks were clinically acceptable. Only two of the ML-generated deltoid masks required major corrections and only one was deemed clinically unacceptable. These corrections had little impact on the deltoid measurements, as the median error in the volume and fatty infiltration measurements was <1% between the ML-generated deltoid masks and the surgeon-corrected deltoid masks. The non-inferiority analysis demonstrates no significant difference between the ML-generated to surgeon-corrected masks relative to inter-surgeon variations. Conclusions: Shoulder expert validation of this CT image analysis tool demonstrates clinically acceptable performance for deltoid auto-segmentation, with no significant differences observed between deltoid image-based measurements derived from the ML generated masks and those corrected by surgeons. These findings suggest that this CT image analysis tool has potential to reliably quantify deltoid muscle size, shape, and quality. Incorporating these CT image-based measurements into the pre-operative planning process may facilitate more personalized treatment decision making, and help orthopedic surgeons make more evidence-based clinical decisions.

Background: When missing data are present in clinical outcomes studies, complete-case analysis (CCA) is often performed, whereby patients with missing data are excluded. While simple, CCA analysis may impart selection bias and reduce statistical power, leading to erroneous statistical results in some cases. However, there exist more rigorous statistical approaches, such as single and multiple imputation, which approximate the associations that would have been present in a full dataset and preserve the study’s power. The purpose of this study is to evaluate how statistical results differ when performed after CCA analysis versus imputation methods. Methods: This simulation study analyzed a sample dataset consisting of 2204 shoulders, with complete datapoints from a larger multicenter total shoulder arthroplasty database. From the sampled dataset of demographics, surgical characteristics, and clinical outcomes, we created five test datasets, ranging from 100 to 2000 shoulders, and simulated 10–50% missingness in the postoperative American Shoulder and Elbow Surgeons (ASES) score and range of motion in four planes in missing completely at random (MCAR), missing at random (MAR), and not missing at random (NMAR) patterns. Missingness in outcomes was remedied using CCA, three single imputation techniques, and two multiple imputation techniques. The imputation performance was evaluated relative to the native complete dataset using the root mean squared error (RMSE) and the mean absolute percentage error (MAPE). We also compared the mean and standard deviation (SD) of the postoperative ASES score and the results of multivariable linear and logistic regression to understand the effects of imputation on the study results. Results: The average overall RMSE and MAPE were similar for MCAR (22.6 and 27.2%) and MAR (19.2 and 17.7%) missingness patterns, but were substantially poorer for NMAR (37.5 and 79.2%); the sample size and the percentage of data missingness minimally affected RMSE and MAPE. Aggregated mean postoperative ASES scores were within 5% of the true value when missing data were remedied with CCA, and all candidate imputation methods for nearly all ranges of sample size and data missingness when data were MCAR or MAR, but not when data were NMAR. When data were MAR, CCA resulted in overestimates of the SD. When data were MCAR or MAR, the accuracy of the regression estimate (β or OR) and its corresponding 95% CI varied substantially based on the sample size and proportion of missing data for multivariable linear regression, but not logistic regression. When data were MAR, the width of the 95% CI was up to 300% larger when CCA was used, whereas most imputation methods maintained the width of the 95% CI within 50% of the true value. Single imputation with k-nearest neighbor (kNN) method and multiple imputation with predictive mean matching (MICE-PMM) best-reproduced point estimates and intervariable relationships resembling the native dataset. Availability of correlated outcome scores improved the RMSE, MAPE, accuracy of the mean postoperative ASES score, and multivariable linear regression model estimates. Conclusions: Complete-case analysis can introduce selection bias when data are MAR, and it results in loss of statistical power, resulting in loss of precision (i.e., expansion of the 95% CI) and predisposition to false-negative findings. Our data demonstrate that imputation can reliably reproduce missing clinical data and generate accurate population estimates that closely resemble results derived from native primary shoulder arthroplasty datasets (i.e., prior to simulated data missingness). Further study of the use of imputation in clinical database research is critical, as the use of CCA may lead to different conclusions in comparison to more rigorous imputation approaches.