Citations

Abstract Rumination syndrome (RS) is a common pediatric disorder of gut–brain interaction. However, a dearth of research on efficacious treatment to help guide clinical decision‐making remains. Thus, relying on well‐established treatment recommendations for other conditions with similar mechanisms or symptom profiles may be helpful; for example, conceptualizing RS treatment targets to address it as a “tic disorder of the gut” could be beneficial. To demonstrate the clinical utility of this conceptualization, we presented two cases that provide broad implications for treating pediatric RS. In both cases, akin to pediatric tic and related disorders, cognitive distress and physical discomfort were alleviated temporarily by rumination episodes. Thus, we recommend a targeted diagnostic assessment, including a functional analysis incorporating a transdiagnostic evaluation of symptoms. Close comanagement with specialty physicians and psychologists is also highly recommended. The patients represent complex yet standard cases seen in pediatric psychology outpatient clinics, suggesting the clinical utility of implications for psychologists and allied health professionals.

Background: Randomized controlled trials (RCTs) evaluating the efficacy of platelet-rich plasma (PRP) for the management of lateral epicondylitis (LE) have been characterized by substantial variability in reported outcomes. The source of this heterogeneity is uncertain. Purpose: To determine the effect of estimated platelet concentration on the efficacy of PRP for the management of LE. Study Design: Systematic review and meta-analysis; Level of evidence, 2. Methods: All RCTs evaluating the efficacy of PRP in managing LE were identified. RCTs were classified according to whether the study documented a platelet concentration factor of PRP representing a greater than 3-fold increase over whole blood or a supraphysiological platelet dose (high-dose vs low-dose PRP). The primary outcome was the mean difference (MD) in the visual analog scale (VAS) score at latest follow-up. Random-effects and mixed-effects meta-analyses were performed, and meta-regression was used to evaluate whether differences in outcomes after treatment with PRP could be explained by differences in the concentration of PRP used. Results: Overall, 13 RCTs with a total of 791 patients were included in this analysis, with 5 that utilized low-dose PRP and 8 that used high-dose PRP. Meta-analysis of VAS scores reported by studies that used high-dose PRP resulted in an MD of −1.31 (95% CI, −1.87 to −0.75) in favor of PRP over all alternative treatment strategies ( P < .001). Meta-analysis of VAS scores reported by studies that used low-dose PRP resulted in an MD of 0.08 (95% CI, −0.51 to 0.68), suggesting no difference in the effect between PRP and all alternative treatment strategies ( P = .79). The platelet concentration factor of PRP used in each RCT was found to be strongly predictive of the VAS score at final follow-up in meta-regression ( P < .001), with 58.5% of the heterogeneity in the outcomes of PRP between studies explained by the platelet concentration factor alone. Conclusion: The platelet concentration of PRP may play a significant role in the outcomes of patients with LE. A direct linear relationship was observed between the platelet concentration factor of PRP used and the magnitude of patient-reported symptom relief after the management of LE with PRP. Clinicians should ensure a supraphysiological platelet concentration when preparing PRP for the management of LE.

AbstractPurposeTo systematically review the literature regarding machine learning in leg length discrepancy (LLD) and to provide insight into the most relevant manuscripts on this topic in order to highlight the importance and future clinical implications of machine learning in the diagnosis and treatment of LLD.MethodsA systematic electronic search was conducted using PubMed, OVID/Medline and Cochrane libraries in accordance with Preferred Reporting Items for Systematic Review and Meta‐Analysis guidelines. Two observers independently screened the abstracts and titles of potential articles.ResultsA total of six studies were identified in the search. All measurements were calculated using standardized anterior‐posterior long‐leg radiographs. Five (83.3%) of the studies used measurements of the femoral length, tibial length and leg length to assess LLD, whereas one (16.6%) study used the iliac crest height difference to quantify LLD. The deep learning models showed excellent reliability in predicting all length measurements with intraclass correlation coefficients ranging from 0.98 to 1.0 and mean absolute error (MAE) values ranging from 0.11 to 0.45 cm. Three studies reported measurements of LLD, and the convolutional neural network model showed the lowest MAE of 0.13 cm in predicting LLD.ConclusionsMachine learning models are effective and efficient in determining LLD. Implementation of these models may reduce cost, improve efficiency and lead to better overall patient outcomes.Clinical RelevanceThis review highlights the potential of deep learning (DL) algorithms for accurate and reliable measurement of lower limb length and leg length discrepancy (LLD) on long‐leg radiographs. The reported mean absolute error and intraclass correlation coefficient values indicate that the performance of the DL models was comparable to that of radiologists, suggesting that DL‐based assessments could potentially be used to automate the measurement of lower limb length and LLD in clinical practice.Level of EvidenceLevel IV.

Purpose To develop a deep learning model for the detection of Segond fractures on anteroposterior (AP) knee radiographs and to compare model performance to that of trained human experts. Methods AP knee radiographs were retrieved from the Hospital for Special Surgery ACL Registry, which enrolled patients between 2009 and 2013. All images corresponded to patients who underwent anterior cruciate ligament reconstruction by 1 of 23 surgeons included in the registry data. Images were categorized into 1 of 2 classes based on radiographic evidence of a Segond fracture and manually annotated. Seventy percent of the images were used to populate the training set, while 20% and 10% were reserved for the validation and test sets, respectively. Images from the test set were used to compare model performance to that of expert human observers, including an orthopaedic surgery sports medicine fellow and a fellowship‐trained orthopaedic sports medicine surgeon with over 10 years of experience. Results A total of 324 AP knee radiographs were retrieved, of which 34 (10.4%) images demonstrated evidence of a Segond fracture. The overall mean average precision (mAP) was 0.985, and this was maintained on the Segond fracture class (mAP = 0.978, precision = 0.844, recall = 1). The model demonstrated 100% accuracy with perfect sensitivity and specificity when applied to the independent testing set and the ability to meet or exceed human sensitivity and specificity in all cases. Compared to an orthopaedic surgery sports medicine fellow, the model required 0.3% of the total time needed to evaluate and classify images in the independent test set. Conclusions A deep learning model was developed and internally validated for Segond fracture detection on AP radiographs and demonstrated perfect accuracy, sensitivity, and specificity on a small test set of radiographs with and without Segond fractures. The model demonstrated superior performance compared with expert human observers. Clinical Relevance Deep learning can be used for automated Segond fracture identification on radiographs, leading to improved diagnosis of easily missed concomitant injuries, including lateral meniscus tears. Automated identification of Segond fractures can also enable large‐scale studies on the incidence and clinical significance of these fractures, which may lead to improved management and outcomes for patients with knee injuries.

AbstractDeep learning has the potential to be one of the most transformative technologies to impact orthopedic surgery. Substantial innovation in this area has occurred over the past 5 years, but clinically meaningful advancements remain limited by a disconnect between clinical and technical experts. That is, it is likely that few orthopedic surgeons possess both the clinical knowledge necessary to identify orthopedic problems, and the technical knowledge needed to implement deep learning‐based solutions. To maximize the utilization of rapidly advancing technologies derived from deep learning models, orthopedic surgeons should understand the steps needed to design, organize, implement, and evaluate a deep learning project and its workflow. Equipping surgeons with this knowledge is the objective of this three‐part editorial review. Part I described the processes involved in defining the problem, team building, data acquisition, curation, labeling, and establishing the ground truth. Building on that, this review (Part II) provides guidance on pre‐processing and augmenting the data, making use of open‐source libraries/toolkits, and selecting the required hardware to implement the pipeline. Special considerations regarding model training and evaluation unique to deep learning models relative to “shallow” machine learning models are also reviewed. Finally, guidance pertaining to the clinical deployment of deep learning models in the real world is provided. As in Part I, the focus is on applications of deep learning for computer vision and imaging.

AbstractDeep learning has a profound impact on daily life. As Orthopedics makes use of this rapid escalation in technology, Orthopedic surgeons will need to take leadership roles on deep learning projects. Moreover, surgeons must possess an understanding of what is necessary to design and implement deep learning‐based project pipelines. This review provides a practical guide for the Orthopedic surgeon to understand the steps needed to design, develop, and deploy a deep learning pipeline for clinical applications. A detailed description of the processes involved in defining the problem, building the team, acquiring and curating the data, labeling the data, establishing the ground truth, pre‐processing and augmenting the data, and selecting the required hardware is provided. In addition, an overview of unique considerations involved in the training and evaluation of deep learning models is provided. This review strives to provide surgeons with the groundwork needed to identify gaps in the clinical landscape that deep learning models may be able to fill and equips them with the knowledge needed to lead an interdisciplinary team through the process of creating novel deep‐learning‐based solutions to fill those gaps.

AbstractMean, median, and mode are among the most basic and consistently used measures of central tendency in statistical analysis and are crucial for simplifying data sets to a single value. However, there is a lack of understanding of when to use each metric and how various factors can impact these values. The aim of this article is to clarify some of the confusion related to each measure and explain how to select the appropriate metric for a given data set. The authors present this work as an educational resource, ensuring that these common statistical concepts are better understood throughout the Orthopedic research community.

AbstractSupervised learning is the most common form of machine learning utilized in medical research. It is used to predict outcomes of interest or classify positive and/or negative cases with a known ground truth. Supervised learning describes a spectrum of techniques, ranging from traditional regression modeling to more complex tree boosting, which are becoming increasingly prevalent as the focus on “big data” develops. While these tools are becoming increasingly popular and powerful, there is a paucity of literature available that describe the strengths and limitations of these different modeling techniques. Typically, there is no formal training for health care professionals in the use of machine learning models. As machine learning applications throughout medicine increase, it is important that physicians and other health care professionals better understand the processes underlying application of these techniques. The purpose of this study is to provide an overview of commonly used supervised learning techniques with recent case examples within the orthopedic literature. An additional goal is to address disparities in the understanding of these methods to improve communication within and between research teams.

Objective The purpose of this study was to compare the results of a biphasic synthetic scaffold (TruFit, Smith & Nephew) to microfracture for the treatment of knee cartilage defects and identify patient- and lesion-specific factors that influence outcomes. Design Prospectively collected data from 132 patients (mean age, 41.8 years; 69% male) with isolated chondral or osteochondral femoral defects treated with biphasic synthetic scaffolds ( n = 66) or microfracture ( n = 66) were reviewed. Clinical outcomes were evaluated longitudinally over 5 years with the Short Form–36 (SF-36), Activities of Daily Living of the Knee Outcome Survey (KOS-ADL), International Knee Documentation Committee (IKDC), and Marx Activity Scale. Cartilage-sensitive magnetic resonance imaging (MRI) was performed to evaluate osseous integration and cartilage fill in a subgroup of patients. Multivariate regression analysis was used to identify predictors of clinical outcomes within the scaffold group. Results Both groups demonstrated clinically significant improvements in knee clinical scores over 5 years ( P < 0.01). There were no significant differences in KOS-ADL and IKDC scores between groups up to 5 years postoperatively. Marx activity level scores in the microfracture group declined over time, while significant improvements in activity level scores were observed in the scaffold group over 5 years ( P < 0.01). Good-quality tissue fill and cartilage isointensity were more often observed in the scaffold group compared with the microfracture group, particularly with longer time intervals. Increasing age, high body mass index, prior microfracture, and traumatic etiology were predictors for inferior outcomes in the scaffold group. Conclusions Activity level and MRI appearance following treatment of cartilage lesions with the biphasic synthetic scaffold were superior to microfracture over time in this nonrandomized, retrospective comparison.

Objective Distal femoral varus osteotomy (DFVO) is an effective treatment for unloading valgus knee malalignment; however, there is limited evidence on the ability for patients to return to athletics following this procedure. The purpose of this study is to report the functional outcomes and rate of return to sport for athletes that underwent DFVO. Design A consecutive series of athletes that had undergone DFVO were retrospectively reviewed. Radiographs were assessed to determine preoperative and postoperative alignment. Institutional registries were used to collect preoperative and postoperative Marx Activity Scale, International Knee Documentation Committee (IKDC) Subjective Knee Evaluation Form scores, and return to sport. Results Thirteen patients (8 males, 5 females) with a mean age of 24 years (range 17-35 years) and a mean follow-up of 43 months (range 24-74 months) were included in the study. Six patients underwent medial closing wedge DFVO versus 7 patients who underwent lateral opening wedge DFVO. Nine of 13 had concomitant chondral, meniscal, or ligamentous procedures performed. The mean alignment correction was 8° (range 5°-13°). All patients were able to successfully return to sport at a mean of 11 months (range 9-13 months). Furthermore, all 13 patients demonstrated an improvement in both Marx Activity Scale (4-11; P < 0.01) and IKDC scores (53-89; P < 0.01) after surgery. Conclusions Correction of valgus knee malalignment through DFVO—either medial closing wedge or lateral opening wedge—can reliably result in improvement in function and return to sport. Concomitant chondral, meniscal, and ligamentous pathology should be addressed.