The Relationship Between Design-Based Lateralization, Humeral bearing design, Polyethylene Angle, and Patient-Related Factors on Surgical Complications after Reverse Shoulder Arthroplasty: A Machine Learning Analysis.

INTRODUCTION: Technological advancements in implant design and surgical technique have focused on diminishing complications and optimizing performance of reverse shoulder arthroplasty (RSA). Despite this, there remains a paucity of literature correlating prosthetic features and clinical outcomes. This investigation utilized a machine learning approach to evaluate the effect of select implant design features and patient-related factors on surgical complications after RSA. METHODS: Over a 16-year period (2004 - 2020), all primary RSA performed at a single institution for elective and traumatic indications with a minimum follow-up of 2 years were identified. Parameters related to implant design evaluated in this study included inlay vs onlay humeral bearing design, glenoid lateralization (medialized or lateralized), humeral lateralization (medialized, minimally lateralized, or lateralized), global lateralization (medialized, minimally lateralized, lateralized, highly lateralized, or very highly lateralized), stem to metallic bearing neck shaft angle (NSA), and polyethylene NSA. Machine learning models predicting surgical complications were constructed for each patient and Shapley additive explanation (SHAP) values were calculated to quantify feature importance. RESULTS: A total of 3,837 RSAs were identified, of which 472 (12.3%) experienced a surgical complication. Those experiencing a surgical complication were more likely to be current smokers (Odds ratio [OR] = 1.71; P = .003), have prior surgery (OR = 1.60; P < .001), have an underlying diagnosis of sequalae of instability (OR = 4.59; P < .001) or non-union (OR = 3.09; P < .001), and required longer OR times (98 vs. 86 minutes; P < .001). Notable implant design features at an increased odds for complications included an inlay humeral component (OR = 1.67; P < .001), medialized glenoid (OR = 1.43; P = .001), medialized humerus (OR = 1.48; P = .004), a minimally lateralized global construct (OR = 1.51; P < .001), and glenohumeral constructs consisting of a medialized glenoid and minimally lateralized humerus (OR = 1.59; P < .001), and a lateralized glenoid and medialized humerus (OR = 2.68; P < .001). Based on patient- and implant-specific features, the machine learning model predicted complications after RSA with an area under the receiver operating characteristic curve (AUC ROC) of 0.61. CONCLUSIONS: This study demonstrated that patient-specific risk factors had a more substantial effect than implant design configurations on the predictive ability of a machine learning model on surgical complications after RSA. However, certain implant features appeared to be associated with a higher odd of surgical complications.

Early Surgery for Partial Tears of the Ulnar Collateral Ligament May Be More Cost-Effective and Result in Longer Playing Careers Than Nonoperative Management for High-Level Baseball Pitchers: A Decision-Analytic Markov Model-Based Analysis.

BACKGROUND: Nonoperative management versus early reconstruction for partial tears of the medial ulnar collateral ligament (MUCL) remains controversial, with the most common treatment options for partial tears consisting of rest, rehabilitation, platelet-rich plasma (PRP), and/or surgical intervention. However, whether the improved outcomes reported for treatments such as MUCL reconstruction (UCLR) or nonoperative management with a series of PRP injections justifies their increased upfront costs remains unknown. PURPOSE: To compare the cost-effectiveness of an initial trial of physical therapy alone, an initial trial of physical therapy plus a series of PRP injections, and early UCLR to determine the preferred cost-effective treatment strategy for young, high-level baseball pitchers with partial tears of the MUCL and with aspirations to continue play at the next level (ie, collegiate and/or professional). STUDY DESIGN: Economic and decision analysis; Level of evidence, 2. METHODS: A Markov chain Monte Carlo probabilistic model was developed to evaluate the outcomes and costs of 1000 young, high-level, simulated pitchers undergoing nonoperative management with and without PRP versus early UCLR for partial MUCL tears. Utility values, return to play rates, and transition probabilities were derived from the published literature. Costs were determined based on the typical patient undergoing each treatment strategy at the authors' institution. Outcome measures included costs, acquired playing years (PYs), and the incremental cost-effectiveness ratio (ICER). RESULTS: The mean total costs resulting from nonoperative management without PRP, nonoperative management with PRP, and early UCLR were $22,520, $24,800, and $43,992, respectively. On average, early UCLR produced an additional 4.0 PYs over the 10-year time horizon relative to nonoperative management, resulting in an ICER of $5395/PY, which falls well below the $50,000 willingness-to-pay threshold. Overall, early UCLR was determined to be the preferred cost-effective strategy in 77.5% of pitchers included in the microsimulation model, with nonoperative management with PRP determined to be the preferred strategy in 15% of pitchers and nonoperative management alone in 7.5% of pitchers. CONCLUSION: Despite increased upfront costs, UCLR is a more cost-effective treatment option for partial tears of the MUCL than an initial trial of nonoperative management for most high-level baseball pitchers.

ChatGPT-4 Performs Clinical Information Retrieval Tasks Utilizing Consistently More Trustworthy Resources Than Does Google Search for Queries Concerning the Latarjet Procedure.

PURPOSE: To assess the ability for ChatGPT-4, an automated Chatbot powered by artificial intelligence (AI), to answer common patient questions concerning the Latarjet procedure for patients with anterior shoulder instability and compare this performance to Google Search Engine. METHODS: Using previously validated methods, a Google search was first performed using the query "Latarjet." Subsequently, the top ten frequently asked questions (FAQs) and associated sources were extracted. ChatGPT-4 was then prompted to provide the top ten FAQs and answers concerning the procedure. This process was repeated to identify additional FAQs requiring discrete-numeric answers to allow for a comparison between ChatGPT-4 and Google. Discrete, numeric answers were subsequently assessed for accuracy based on the clinical judgement of two fellowship-trained sports medicine surgeons blinded to search platform. RESULTS: Mean (±standard deviation) accuracy to numeric-based answers were 2.9±0.9 for ChatGPT-4 versus 2.5±1.4 for Google (p=0.65). ChatGPT-4 derived information for answers only from academic sources, which was significantly different from Google Search Engine (p=0.003), which used only 30% academic sources and websites from individual surgeons (50%) and larger medical practices (20%). For general FAQs, 40% of FAQs were found to be identical when comparing ChatGPT-4 and Google Search Engine. In terms of sources used to answer these questions, ChatGPT-4 again used 100% academic resources, while Google Search Engine used 60% academic resources, 20% surgeon personal websites, and 20% medical practices (p=0.087). CONCLUSION: ChatGPT-4 demonstrated the ability to provide accurate and reliable information about the Latarjet procedure in response to patient queries, using multiple academic sources in all cases. This was in contrast to Google Search Engine, which more frequently used single surgeon and large medical practice websites. Despite differences in the resources accessed to perform information retrieval tasks, the clinical relevance and accuracy of information provided did not significantly differ between ChatGPT-4 and Google Search Engine.

The Number of Patients Lost to Follow-Up May Exceed the Fragility Index of a Randomized Controlled Trial Without Reversing Statistical Significance: A Systematic Review and Statistical Model.

PURPOSE: To (1) analyze trends in the publishing of statistical fragility index (FI)-based systematic reviews in the orthopaedic literature, including the prevalence of misleading or inaccurate statements related to the statistical fragility of randomized controlled trials (RCTs) and patients lost to follow-up (LTF), and (2) determine whether RCTs with relatively "low" FIs are truly as sensitive to patients LTF as previously portrayed in the literature. METHODS: All FI-based studies published in the orthopaedic literature were identified using the Cochrane Database of Systematic Reviews, Web of Science Core Collection, PubMed, and MEDLINE databases. All articles involving application of the FI or reverse FI to study the statistical fragility of studies in orthopaedics were eligible for inclusion in the study. Study characteristics, median FIs and sample sizes, and misleading or inaccurate statements related to the FI and patients LTF were recorded. Misleading or inaccurate statements-defined as those basing conclusions of trial fragility on the false assumption that adding patients LTF back to a trial has the same statistical effect as existing patients in a trial experiencing the opposite outcome-were determined by 2 authors. A theoretical RCT with a sample size of 100, P = .006, and FI of 4 was used to evaluate the difference in effect on statistical significance between flipping outcome events of patients already included in the trial (FI) and adding patients LTF back to the trial to show the true sensitivity of RCTs to patients LTF. RESULTS: Of the 39 FI-based studies, 37 (95%) directly compared the FI with the number of patients LTF. Of these 37 studies, 22 (59%) included a statement regarding the FI and patients LTF that was determined to be inaccurate or misleading. In the theoretical RCT, a reversal of significance was not observed until 7 patients LTF (nearly twice the FI) were added to the trial in the distribution of maximal significance reversal. CONCLUSIONS: The claim that any RCT in which the number of patients LTF exceeds the FI could potentially have its significance reversed simply by maintaining study follow-ups is commonly inaccurate and prevalent in orthopaedic studies applying the FI. Patients LTF and the FI are not equivalent. The minimum number of patients LTF required to flip the significance of a typical RCT was shown to be greater than the FI, suggesting that RCTs with relatively low FIs may not be as sensitive to patients LTF as previously portrayed in the literature; however, only a holistic approach that considers the context in which the trial was conducted, potential biases, and study results can determine the merits of any particular RCT. CLINICAL RELEVANCE: Surgeons may benefit from re-examining their interpretation of prior FI reviews that have made claims of substantial RCT fragility based on comparisons between the FI and patients LTF; it is possible the results are more robust than previously believed.

What Patients Say About Their Orthopaedic Hand and Wrist Surgeons: A Qualitative Analysis of Negative Reviews on Yelp.

Background Patients often turn to online reviews as a source of information to inform their decisions regarding care. Existing literature has analyzed factors associated with positive online patient ratings among hand and wrist surgeons. However, there is limited in-depth analysis of factors associated with low patient satisfaction for hand and wrist surgeons. The focus of this study is to examine and characterize extremely negative reviews of hand and wrist surgeons on Yelp.com. Methods A search was performed using the keywords "hand surgery" on Yelp.com for eight major metropolitan areas including Washington DC, Dallas, New York, Phoenix, Los Angeles, San Francisco, Boston, and Seattle. Only single-star reviews (out of a possible 5 stars) of hand and wrist surgeons were included. The complaints in the 1-star reviews were then categorized into clinical and nonclinical categories. Result A total of 233 single-star reviews were included for analysis, which resulted in 468 total complaints. Of these complaints, 81 (18.8%) were clinically related and 351 (81.3%) were nonclinical in nature. The most common clinical complaints were for complication (24 complaints, 6%), misdiagnosis (16 complaints, 4%), unclear treatment plan (16 complaints, 4%), and uncontrolled pain (15 complaints, 3%). The most common nonclinical complaints were for physician bedside manner (93 complaints, 22%), financially related (80 complaints, 19%), unprofessional nonclinical staff (61 complaints, 14%), and wait time (46 complaints, 11%). The difference in the number of complaints for surgical and nonsurgical patients was statistically significant ( p < 0.05) for complication and uncontrolled pain. Clinical Relevance Patient satisfaction is dependent on a multitude of clinical and nonclinical factors. An awareness of online physician ratings is essential for hand and wrist surgeons to maintain and improve patient care and patient satisfaction. We believe the results of our study could be used to further improve the quality of care provided by hand and wrist surgeons.

A practical guide to the development and deployment of deep learning models for the orthopaedic surgeon: Part III, focus on registry creation, diagnosis, and data privacy.

Deep learning is a subset of artificial intelligence (AI) with enormous potential to transform orthopaedic surgery. As has already become evident with the deployment of Large Language Models (LLMs) like ChatGPT (OpenAI Inc.), deep learning can rapidly enter clinical and surgical practices. As such, it is imperative that orthopaedic surgeons acquire a deeper understanding of the technical terminology, capabilities and limitations associated with deep learning models. The focus of this series thus far has been providing surgeons with an overview of the steps needed to implement a deep learning-based pipeline, emphasizing some of the important technical details for surgeons to understand as they encounter, evaluate or lead deep learning projects. However, this series would be remiss without providing practical examples of how deep learning models have begun to be deployed and highlighting the areas where the authors feel deep learning may have the most profound potential. While computer vision applications of deep learning were the focus of Parts I and II, due to the enormous impact that natural language processing (NLP) has had in recent months, NLP-based deep learning models are also discussed in this final part of the series. In this review, three applications that the authors believe can be impacted the most by deep learning but with which many surgeons may not be familiar are discussed: (1) registry construction, (2) diagnostic AI and (3) data privacy. Deep learning-based registry construction will be essential for the development of more impactful clinical applications, with diagnostic AI being one of those applications likely to augment clinical decision-making in the near future. As the applications of deep learning continue to grow, the protection of patient information will become increasingly essential; as such, applications of deep learning to enhance data privacy are likely to become more important than ever before. Level of Evidence: Level IV.

Sex differences in patients with and without high-risk factors associated with aneurysmal subarachnoid hemorrhage.

BACKGROUND: Controversy remains regarding the appropriate screening for intracranial aneurysms or for the treatment of aneurysmal subarachnoid hemorrhage (aSAH) for patients without known high-risk factors for rupture. This study aimed to assess how sex affects both aSAH presentation and outcomes for aSAH treatment. METHOD: A retrospective cohort study was conducted of all patients treated at a single institution for an aSAH during a 12-year period (August 1, 2007-July 31, 2019). An analysis of women with and without high-risk factors was performed, including a propensity adjustment for a poor neurologic outcome (modified Rankin Scale [mRS] score > 2) at follow-up. RESULTS: Data from 1014 patients were analyzed (69% [n = 703] women). Women were significantly older than men (mean ± SD, 56.6 ± 14.1 years vs 53.4 ± 14.2 years, p < 0.001). A significantly lower percentage of women than men had a history of tobacco use (36.6% [n = 257] vs 46% [n = 143], p = 0.005). A significantly higher percentage of women than men had no high-risk factors for aSAH (10% [n = 70] vs 5% [n = 16], p = 0.01). The percentage of women with an mRS score > 2 at the last follow-up was significantly lower among those without high-risk factors (34%, 24/70) versus those with high-risk factors (53%, 334/633) (p = 0.004). Subsequent propensity-adjusted analysis (adjusted for age, Hunt and Hess grade, and Fisher grade) found no statistically significant difference in the odds of a poor outcome for women with or without high-risk factors for aSAH (OR = 0.7, 95% CI = 0.4-1.2, p = 0.18). CONCLUSIONS: A higher percentage of women versus men with aSAH had no known high-risk factors for rupture, supporting more aggressive screening and management of women with unruptured aneurysms.

Diagnostic Performance of Artificial Intelligence for Detection of Scaphoid and Distal Radius Fractures: A Systematic Review.

PURPOSE: To review the existing literature to (1) determine the diagnostic efficacy of artificial intelligence (AI) models for detecting scaphoid and distal radius fractures and (2) compare the efficacy to human clinical experts. METHODS: PubMed, OVID/Medline, and Cochrane libraries were queried for studies investigating the development, validation, and analysis of AI for the detection of scaphoid or distal radius fractures. Data regarding study design, AI model development and architecture, prediction accuracy/area under the receiver operator characteristic curve (AUROC), and imaging modalities were recorded. RESULTS: A total of 21 studies were identified, of which 12 (57.1%) used AI to detect fractures of the distal radius, and nine (42.9%) used AI to detect fractures of the scaphoid. AI models demonstrated good diagnostic performance on average, with AUROC values ranging from 0.77 to 0.96 for scaphoid fractures and from 0.90 to 0.99 for distal radius fractures. Accuracy of AI models ranged between 72.0% to 90.3% and 89.0% to 98.0% for scaphoid and distal radius fractures, respectively. When compared to clinical experts, 13 of 14 (92.9%) studies reported that AI models demonstrated comparable or better performance. The type of fracture influenced model performance, with worse overall performance on occult scaphoid fractures; however, models trained specifically on occult fractures demonstrated substantially improved performance when compared to humans. CONCLUSIONS: AI models demonstrated excellent performance for detecting scaphoid and distal radius fractures, with the majority demonstrating comparable or better performance compared with human experts. Worse performance was demonstrated on occult fractures. However, when trained specifically on difficult fracture patterns, AI models demonstrated improved performance. CLINICAL RELEVANCE: AI models can help detect commonly missed occult fractures while enhancing workflow efficiency for distal radius and scaphoid fracture diagnoses. As performance varies based on fracture type, future studies focused on wrist fracture detection should clearly define whether the goal is to (1) identify difficult-to-detect fractures or (2) improve workflow efficiency by assisting in routine tasks.

Medical Imaging Applications Developed Using Artificial Intelligence Demonstrate High Internal Validity Yet Are Limited in Scope and Lack External Validation.

PURPOSE: To (1) review definitions and concepts necessary to interpret applications of deep learning (DL; a domain of artificial intelligence that leverages neural networks to make predictions on media inputs such as images) and (2) identify knowledge and translational gaps in the literature to provide insight into specific areas for improvement as adoption of this technology continues. METHODS: A comprehensive search of the literature was performed in December 2023 for articles regarding the use of DL in sports medicine. For each study, information regarding the joint of focus, specific anatomic structure/pathology to which DL was applied, imaging modality utilized, source of images used for model training and testing, data set size, model performance, and whether the DL model was externally validated was recorded. A numerical scale was used to rate each DL model's clinical impact, with 1 corresponding to proof-of-concept studies with little to no direct clinical impact and 5 corresponding to practice-changing clinical impact and ready for clinical deployment. RESULTS: Fifty-five studies were identified, all of which were published within the past 5 years, while 82% were published within the past 3 years. Of the DL models identified, 84% were developed for classification tasks, 9% for automated measurements, and 7% for segmentation. A total of 62% of studies utilized magnetic resonance imaging as the imaging modality, 25% radiographs, and 7% ultrasound, while 1 study each used computed tomography, arthroscopic images, or arthroscopic video. Sixty-five percent of studies focused on the detection of tears (anterior cruciate ligament [ACL], rotator cuff [RC], and meniscus). The diagnostic performance of ACL tears, as determined by the area under the receiver operator curve (AUROC), ranged from 0.81 to 0.99 for ACL tears (excellent to near perfect), 0.83 to 0.94 for RC tears (excellent), and from 0.75 to 0.96 for meniscus tears (acceptable to excellent). In addition, 3 studies focused on detection of cartilage lesions had AUROC ranging from 0.90 to 0.92 (excellent performance). However, only 4 (7%) studies externally validated their models, suggesting that they may not be generalizable or may not perform well when applied to populations other than that used to develop the model. Finally, the mean clinical impact score was 2 (range, 1-3) on scale of 1 to 5, corresponding to limited clinical applicability. CONCLUSIONS: DL models in orthopaedic sports medicine show generally excellent performance (high internal validity) but require external validation to facilitate clinical deployment. In addition, current models have low clinical applicability and fail to advance the field due to a focus on routine tasks and a narrow conceptual framework. LEVEL OF EVIDENCE: Level IV, scoping review of Level I to IV studies.

Costs of revision operations for distal junctional kyphosis following thoracic posterior spinal fusion for adolescent idiopathic scoliosis.

PURPOSE: To assess direct costs and risks associated with revision operations for distal junctional kyphosis/failure (DJK) following thoracic posterior spinal instrumented fusions (TPSF) for adolescent idiopathic scoliosis (AIS). METHODS: Children who underwent TPSF for AIS by a single surgeon (2014-2020) were reviewed. Inclusion criteria were minimum follow-up of 2 years, thoracolumbar posterior instrumented fusion with a lower instrumented vertebra (LIV) cranial to L2. Patients who developed DJK requiring revision operations were identified and compared with those who did not develop DJK. RESULTS: Seventy-nine children were included for analysis. Of these, 6.3% developed DJK. Average time to revision was 20.8 ± 16.2 months. Comparing index operations, children who developed DJK had significantly greater BMIs, significantly lower thoracic kyphosis postoperatively, greater post-operative lumbar Cobb angles, and significantly more LIVs cranial to the sagittal stable vertebrae (SSV), despite having statistically similar pre-operative coronal and sagittal alignment parameters and operative details compared with non-DJK patients. Revision operations for DJK, when compared with index operations, involved significantly fewer levels, longer operative times, greater blood loss, and longer hospital lengths of stay. These factors resulted in significantly greater direct costs for revision operations for DJK ($76,883 v. $46,595; p < 0.01). CONCLUSIONS: In this single-center experience, risk factors for development of DJK were greater BMI, lower post-operative thoracic kyphosis, and LIV cranial to SSV. As revision operations for DJK were significantly more costly than index operations, all efforts should be aimed at strategies to prevent DJK in the AIS population.

Early Arthroscopic Debridement May Be More Cost-Effective Than Nonoperative Management for Symptomatic Osteochondritis Dissecans Lesions of the Capitellum.

Purpose: To compare the cost-effectiveness of an initial trial of nonoperative treatment to that of early arthroscopic debridement for stable osteochondritis dissecans (OCD) lesions of the capitellum. Methods: A Markov Chain Monte Carlo probabilistic model was developed to evaluate the outcomes and costs of 1,000 simulated patients undergoing nonoperative management versus early arthroscopic debridement for stable OCD lesions of the capitellum. Health utility values, treatment success rates, and transition probabilities were derived from the published literature. Costs were determined on the basis of the typical patient undergoing each treatment strategy at our institution. Outcome measures included costs, quality-adjusted life-years (QALYs), and the incremental cost-effectiveness ratio (ICER). Results: Mean total costs resulting from nonoperative management and early arthroscopic debridement were $5,330 and $21,672, respectively. On average, early arthroscopic debridement produced an additional 0.64 QALYS, resulting in an ICER of $25,245/QALY, which falls well below the widely accepted $50,000 willingness-to-pay (WTP) threshold. Overall, early arthroscopic debridement was determined to be the preferred cost-effective strategy in 69% of patients included in the microsimulation model. Conclusion: Results of the Monte Carlo microsimulation and probabilistic sensitivity analysis demonstrated early arthroscopic debridement to be a cost-effective treatment strategy for the majority of stable OCD lesions of the capitellum. Although early arthroscopic debridement was associated with higher total costs, the increase in QALYS that resulted from early surgery was enough to justify the cost difference based on an ICER substantially below the $50,000 WTP threshold. Level of Evidence: Level III, economic computer simulation model.

Fenestrated Aneurysm Clip Trigeminal Decompression after CyberKnife Treatment Failure.

Objective This article describes a novel technique for trigeminal nerve decompression in the setting of refractory trigeminal neuralgia (TN). Design Technical note with an illustrative case example and operative video. Setting Outpatient, inpatient, and operating room of a quaternary neurosurgical referral center. Participant A woman in her early 70s who had previously undergone linear accelerator-based stereotactic radiotherapy (i.e., CyberKnife) and achieved 2 years of partial pain relief. However, facial pain, numbness, and parasympathetic dysfunction returned and became unbearable. Main Outcome Measure Durable relief of TN. Results Microvascular decompression was recommended for refractory TN. Intraoperatively, the trigeminal nerve was markedly attenuated from previous irradiation, with the superior cerebellar artery (SCA) loop embedded in the nerve at its root entry zone. The arterial loop was mobilized into a new position superior to the nerve, thus liberating it from the impingement. The tentorium was incised, and a fenestrated aneurysm clip was positioned such that the SCA loop was transmitted via the fenestration. The clip was applied across the tentorium, thus suspending the artery in a kink-free orientation that made no contact with the nerve. Conclusion This procedure provided excellent neurovascular decompression without placing mechanical strain on the nerve, relieving the patient's persistent postirradiation TN. The technique could have broader applications for other challenging or atypical microvascular decompression procedures.

Vitamin D deficiency promotes intracranial aneurysm rupture.

Intracranial aneurysm rupture causes severe disability and high mortality. Epidemiological studies show a strong association between decreased vitamin D levels and an increase in aneurysm rupture. However, the causality and mechanism remain largely unknown. In this study, we tested whether vitamin D deficiency promotes aneurysm rupture and examined the underlying mechanism for the protective role of vitamin D against the development of aneurysm rupture utilizing a mouse model of intracranial aneurysm. Mice consuming a vitamin D-deficient diet had a higher rupture rate than mice with a regular diet. Vitamin D deficiency increased proinflammatory cytokines in the cerebral arteries. Concurrently, vitamin D receptor knockout mice had a higher rupture rate than the corresponding wild-type littermates. The vitamin D receptors on endothelial and vascular smooth muscle cells, but not on hematopoietic cells, mediated the effect of aneurysm rupture. Our results establish that vitamin D protects against the development of aneurysmal rupture through the vitamin D receptors on vascular endothelial and smooth muscle cells. Vitamin D supplementation may be a viable pharmacologic therapy for preventing aneurysm rupture.

CBCT-Based synthetic CT image generation using conditional denoising diffusion probabilistic model.

BACKGROUND: Daily or weekly cone-beam computed tomography (CBCT) scans are commonly used for accurate patient positioning during the image-guided radiotherapy (IGRT) process, making it an ideal option for adaptive radiotherapy (ART) replanning. However, the presence of severe artifacts and inaccurate Hounsfield unit (HU) values prevent its use for quantitative applications such as organ segmentation and dose calculation. To enable the clinical practice of online ART, it is crucial to obtain CBCT scans with a quality comparable to that of a CT scan. PURPOSE: This work aims to develop a conditional diffusion model to perform image translation from the CBCT to the CT distribution for the image quality improvement of CBCT. METHODS: The proposed method is a conditional denoising diffusion probabilistic model (DDPM) that utilizes a time-embedded U-net architecture with residual and attention blocks to gradually transform the white Gaussian noise sample to the target CT distribution conditioned on the CBCT. The model was trained on deformed planning CT (dpCT) and CBCT image pairs, and its feasibility was verified in brain patient study and head-and-neck (H&N) patient study. The performance of the proposed algorithm was evaluated using mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and normalized cross-correlation (NCC) metrics on generated synthetic CT (sCT) samples. The proposed method was also compared to four other diffusion model-based sCT generation methods. RESULTS: In the brain patient study, the MAE, PSNR, and NCC of the generated sCT were 25.99 HU, 30.49 dB, and 0.99, respectively, compared to 40.63 HU, 27.87 dB, and 0.98 of the CBCT images. In the H&N patient study, the metrics were 32.56 HU, 27.65 dB, 0.98 and 38.99 HU, 27.00, 0.98 for sCT and CBCT, respectively. Compared to the other four diffusion models and one Cycle generative adversarial network (Cycle GAN), the proposed method showed superior results in both visual quality and quantitative analysis. CONCLUSIONS: The proposed conditional DDPM method can generate sCT from CBCT with accurate HU numbers and reduced artifacts, enabling accurate CBCT-based organ segmentation and dose calculation for online ART.

A Review of Preoperative Embolization Effectiveness in Patients With Arteriovenous Malformations.

BACKGROUND AND OBJECTIVES: Preoperative embolization of arteriovenous malformations (AVMs) remains controversial. This study sought to analyze the cost-effectiveness of preoperative embolization of AVMs. METHODS: Patients who underwent AVM resection at a single institute (January 1, 2015-December 31, 2020) were analyzed. Patients with preoperative embolization (embolization cohort) were compared with those without preoperative embolization (nonembolization cohort). Cost-effectiveness score (CE) was the primary outcome of interest and was determined by dividing the total 1-year cost by effectiveness, which was derived from a validated preoperative to last follow-up change in the modified Rankin Scale utility score. A lower CE signifies a more cost-effective treatment strategy. RESULTS: Of 188 patients, 88 (47%) underwent preoperative embolization. The mean (SD) total cost was higher in the embolization group than in the nonembolization group ($117 594 [$102 295] vs $84 348 [$82 326]; P < .001). The mean (SD) CE was higher in the embolization cohort ($336 476 [$1 303 842]) than in the nonembolization cohort ($100 237 [$246 255]; P < .001). Among patients with Spetzler-Martin (SM) grade I and II AVMs, the mean (SD) CE was higher in the embolization (n = 40) than in the nonembolization (n = 72) cohort ($164 950 [$348 286] vs $69 021 [$114 938]; P = .004). Among patients with SM grade III AVMs, the mean (SD) CE was lower in the embolization (n = 33) than in the nonembolization (n = 25) cohort ($151 577 [$219 130] vs $189 195 [$446 335]; P = .006). The mean (SD) CE was not significantly different between cohorts among patients with higher-grade AVMs (embolization cohort [n = 3] vs nonembolization cohort [n = 15]: $503 639 [$776 492] vs $2 048 419 [$4 794 758]; P = .49). The mean CE for embolized SM grade III aneurysms was nonsignificant in the ruptured group; however, for the unruptured group, CE was significantly higher in the embolization cohort (n = 26; $160 871 [$240 535]) relative to the nonembolization cohort (n = 15; $108 152 [$166 446]) ( P = .006). CONCLUSION: Preoperative embolization was cost-effective for patients with SM grade III AVMs but not for patients with lower-grade AVMs.

The 50 Most Cited Publications in Adipose-Derived "Stem Cell Therapies" with Application in Orthopaedic Surgery.

Stem cell therapies have become widely popular in orthopaedic surgery, with a recent interest in adipose-derived therapeutics. Adipose-derived mesenchymal signaling cells (ADSCs) and micronized adipose tissue (MAT) are unique therapies derived from different processing methods. Characterizing the most influential studies in lipoaspirate research can help clarify controversies in definitions, identify core literature, and further collective knowledge for educational purposes. The Science Citation Index Expanded subsection of the Web of Science Core Collection was systematically searched to identify the top 50 most cited publications (based on citation/year) on orthopaedic ADSCs or MAT research. Publication and study characteristics were extracted and reported using descriptive statistics. Level of evidence was assessed for applicable studies, and Spearman correlations were calculated to assess the relationship between citation data and level of evidence. The top 50 articles were published between the years 2003 and 2020, with 78% published in the year 2010 or later. The mean number of citations was 103.1 ± 81.1. The mean citation rate was 12.4 ± 6.0 citations per year. Of the 21 studies for which level of evidence was assessed, the majority were level III (10, 47.6%). The single study design most common among the top 50 cited articles was in vitro basic science studies (17 studies, 34%). Twenty-nine articles (58%) were classified as basic science or translational. Application to treat knee osteoarthritis was the most common focus of studies (14 studies, 28%), followed by in vitro analysis of growth factor and cell signaling markers (11 studies, 22%). No correlation was found between rank, citation rate, or year of publication and level of evidence. This study provides a current landscape on the most cited articles in lipoaspirates in orthopaedic surgery. With the expansion of ADSCs and MAT in the past two decades, this study provides the first historical landmark of the literature and a launching point for future research. Studies should explicitly state their processing methodology and whether their study investigates ADSCs or MAT to avoid misinformation.

A Machine Learning Model Demonstrates Excellent Performance in Predicting Subscapularis Tears Based on Pre-Operative Imaging Parameters Alone.

PURPOSE: To develop a machine learning model capable of identifying subscapularis tears before surgery based on imaging and physical examination findings. METHODS: Between 2010 and 2020, 202 consecutive shoulders underwent arthroscopic rotator cuff repair by a single surgeon. Patient demographics, physical examination findings (including range of motion, weakness with internal rotation, lift/push-off test, belly press test, and bear hug test), and imaging (including direct and indirect signs of tearing, biceps status, fatty atrophy, cystic changes, and other similar findings) were included for model creation. RESULTS: Sixty percent of the shoulders had partial or full thickness tears of the subscapularis verified during surgery (83% of these were upper third). Using only preoperative imaging-related parameters, the XGBoost model demonstrated excellent performance at predicting subscapularis tears (c-statistic, 0.84; accuracy, 0.85; F1 score, 0.87). The top 5 features included direct signs related to the presence of tearing as evidenced on magnetic resonance imaging (MRI) (changes in tendon morphology and signal), as well as the quality of the MRI and biceps pathology. CONCLUSIONS: In this study, machine learning was successful in predicting subscapularis tears by MRI alone in 85% of patients, and this accuracy did not decrease by isolating the model to the top features. The top five features included direct signs related to the presence of tearing as evidenced on MRI (changes in tendon morphology and signal), as well as the quality of the MRI and biceps pathology. Last, in advanced modeling, the addition of physical examination or patient characteristics did not make a significant difference in the predictive ability of this model. LEVEL OF EVIDENCE: Level III, diagnostic case-control study.

Deep learning to automatically classify very large sets of preoperative and postoperative shoulder arthroplasty radiographs.

BACKGROUND: Joint arthroplasty registries usually lack information on medical imaging owing to the laborious process of observing and recording, as well as the lack of standard methods to transfer the imaging information to the registries, which can limit the investigation of various research questions. Artificial intelligence (AI) algorithms can automate imaging-feature identification with high accuracy and efficiency. With the purpose of enriching shoulder arthroplasty registries with organized imaging information, it was hypothesized that an automated AI algorithm could be developed to classify and organize preoperative and postoperative radiographs from shoulder arthroplasty patients according to laterality, radiographic projection, and implant type. METHODS: This study used a cohort of 2303 shoulder radiographs from 1724 shoulder arthroplasty patients. Two observers manually labeled all radiographs according to (1) laterality (left or right), (2) projection (anteroposterior, axillary, or lateral), and (3) whether the radiograph was a preoperative radiograph or showed an anatomic total shoulder arthroplasty or a reverse shoulder arthroplasty. All these labeled radiographs were randomly split into developmental and testing sets at the patient level and based on stratification. By use of 10-fold cross-validation, a 3-task deep-learning algorithm was trained on the developmental set to classify the 3 aforementioned characteristics. The trained algorithm was then evaluated on the testing set using quantitative metrics and visual evaluation techniques. RESULTS: The trained algorithm perfectly classified laterality (F1 scores [harmonic mean values of precision and sensitivity] of 100% on the testing set). When classifying the imaging projection, the algorithm achieved F1 scores of 99.2%, 100%, and 100% on anteroposterior, axillary, and lateral views, respectively. When classifying the implant type, the model achieved F1 scores of 100%, 95.2%, and 100% on preoperative radiographs, anatomic total shoulder arthroplasty radiographs, and reverse shoulder arthroplasty radiographs, respectively. Visual evaluation using integrated maps showed that the algorithm focused on the relevant patient body and prosthesis parts for classification. It took the algorithm 20.3 seconds to analyze 502 images. CONCLUSIONS: We developed an efficient, accurate, and reliable AI algorithm to automatically identify key imaging features of laterality, imaging view, and implant type in shoulder radiographs. This algorithm represents the first step to automatically classify and organize shoulder radiographs on a large scale in very little time, which will profoundly enrich shoulder arthroplasty registries.

Characterization of erythroferrone oligomerization and its impact on BMP antagonism.

Hepcidin, a peptide hormone that negatively regulates iron metabolism, is expressed by bone morphogenetic protein (BMP) signaling. Erythroferrone (ERFE) is an extracellular protein that binds and inhibits BMP ligands, thus positively regulating iron import by indirectly suppressing hepcidin. This allows for rapid erythrocyte regeneration after blood loss. ERFE belongs to the C1Q/TNF-related protein family and is suggested to adopt multiple oligomeric forms: a trimer, a hexamer, and a high molecular weight species. The molecular basis for how ERFE binds BMP ligands and how the different oligomeric states impact BMP inhibition are poorly understood. In this study, we demonstrated that ERFE activity is dependent on the presence of stable dimeric or trimeric ERFE and that larger species are dispensable for BMP inhibition. Additionally, we used an in silico approach to identify a helix, termed the ligand-binding domain, that was predicted to bind BMPs and occlude the type I receptor pocket. We provide evidence that the ligand-binding domain is crucial for activity through luciferase assays and surface plasmon resonance analysis. Our findings provide new insight into how ERFE oligomerization impacts BMP inhibition, while identifying critical molecular features of ERFE essential for binding BMP ligands.