Oeding, Jacob F.

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.

Study Design Retrospective comparative study. Objective To compare patient-reported physical activity between anterior thoracic vertebral body tethering and posterior lumbar spine tethering (ATVBT/PLST) and posterior spinal instrumentation and fusion (PSIF) with minimum 2 year follow-up. Methods Consecutive skeletally immature patients with idiopathic scoliosis and a thoracic and lumbar curve magnitude ≥40° who underwent either ATVBT/PLST or PSIF from 2015-2019 were included. The primary outcome was rate of returning to sport. Secondary outcomes included ability to bend and satisfaction with sport performance as well as weeks until return to sport, school, physical education (PE) classes, and running. Results Ten patients underwent ATVBT/PLST and 12 underwent PSIF. ATVBT/PLST patients reported significantly faster return to sport (13.5 weeks vs 27.9 weeks, P = .04), running (13.3 weeks vs 28.8 weeks, P = .02), and PE class (12.6 weeks vs 26.2 weeks, P = .04) compared to PSIF patients. ATVBT/PLST patients reported that they had to give up activities due to their ability to bend at lower rates than PSIF patients while reporting “no changes” in their ability to bend after surgery at higher rates than PSIF patients (0% vs 4% giving up activities and 70% vs 0% reporting no changes in bending ability for ATVBT/PLST and PSIF, respectively, P = .01). Compared to PSIF patients, ATVBT/PLST patients experienced less main thoracic and thoracolumbar/lumbar curve correction at most recent follow-up (thoracic: 41 ± 19% vs 69 ± 18%, P = .001; thoracolumbar/lumbar: 59 ± 25% vs 78 ± 15%, P = .02). No significant differences in the number of revision surgeries were observed between ATVBT/PLST and PSIF patients (4 (40%) and 1 (8%) for ATVBT/PLST and PSIF, respectively, P = .221). Conclusions ATVBT/PLST patients reported significantly faster rates of returning to sport, running, and PE. In addition, ATVBT/PLST patients were less likely to have to give up activities due to bending ability after surgery and reported no changes in their ability to bend after surgery more frequently than PSIF patients. However, the overall rate of return to the same or higher level of sport participation was high amongst both groups, with no significant difference observed between ATVBT/PLST and PSIF patients.

Case: This report describes the case of an athletic 12-year-old boy who presented with a 64° left proximal humeral varus angulation deformity and physeal bar secondary to multiple operations for a proximal humeral metaphyseal cystic lesion and pathologic fracture. Using a combined lateral closing and medial opening-wedge osteotomy, a 68° valgus correction was achieved with excellent clinical and functional outcomes at 16-month follow-up. Forward flexion increased from 120 to 170° preoperatively to postoperatively while abduction improved from 110° preoperatively to 170° postoperatively. Conclusion: A combined lateral closing and medial opening-wedge osteotomy of the proximal humerus can successfully treat cases of extreme proximal humerus varus in the growing shoulder where unilateral or dome osteotomies are not suitable.

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. Conclusions 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.

Purpose To perform a systematic review of the literature to evaluate (1) activity level and knee function, (2) reoperation and failure rates, and (3) risk factors for reoperation and failure of autologous osteochondral transfer (AOT) at long‐term follow‐up. Methods A comprehensive review of the long‐term outcomes of AOT was performed. Studies reported on activity‐based outcomes (Tegner Activity Scale) and clinical outcomes (Lysholm score and International Knee Documentation Committee score). Reoperation and failure rates as defined by the publishing authors were recorded for each study. Modified Coleman Methodology Scores were calculated to assess study methodological quality. Results Twelve studies with a total of 495 patients and an average age of 32.5 years at the time of surgery and a mean follow‐up of 15.1 years (range, 10.4‐18.0 years) were included. The mean defect size was 3.2 cm 2 (range, 1.9‐6.9 cm 2 ). The mean duration of symptoms before surgery was 5.1 years. Return to sport rates ranged from 86% to 100%. Conversion to arthroplasty rates ranged from 0% to 16%. The average preoperative International Knee Documentation Committee scores ranged from 32.9 to 36.8, and the average postoperative International Knee Documentation Committee scores at final follow‐up ranged from 66.3 to 77.3. The average preoperative Lysholm scores ranged from 44.5 to 56.0 and the average postoperative Lysholm scores ranged from 70.0 to 96.5. The average preoperative Tegner scores ranged from 2.5 to 3.0, and the average postoperative scores ranged from 4.1 to 7.0. Conclusions AOT of the knee resulted in high rates of return to sport with correspondingly low rates of conversion to arthroplasty at long‐term follow‐up. In addition, AOT demonstrated significant improvements in long‐term patient‐reported outcomes from baseline. Level of Evidence Level IV, systematic review of Level I‐IV studies.

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.

Purpose To evaluate the cost‐effectiveness of 3 isolated meniscal repair (IMR) treatment strategies: platelet‐rich plasma (PRP)–augmented IMR, IMR with a marrow venting procedure (MVP), and IMR without biological augmentation. Methods A Markov model was developed to evaluate the baseline case: a young adult patient meeting the indications for IMR. Health utility values, failure rates, and transition probabilities were derived from the published literature. Costs were determined based on the typical patient undergoing IMR at an outpatient surgery center. Outcome measures included costs, quality‐adjusted life‐years (QALYs), and the incremental cost‐effectiveness ratio (ICER). Results Total costs of IMR with an MVP were $8,250; PRP‐augmented IMR, $12,031; and IMR without PRP or an MVP, $13,326. PRP‐augmented IMR resulted in an additional 2.16 QALYs, whereas IMR with an MVP produced slightly fewer QALYs, at 2.13. Non‐augmented repair produced a modeled gain of 2.02 QALYs. The ICER comparing PRP‐augmented IMR versus MVP‐augmented IMR was $161,742/QALY, which fell well above the $50,000 willingness‐to‐pay threshold. Conclusions IMR with biological augmentation (MVP or PRP) resulted in a higher number of QALYs and lower costs than non‐augmented IMR, suggesting that biological augmentation is cost‐effective. Total costs of IMR with an MVP were significantly lower than those of PRP‐augmented IMR, whereas the number of additional QALYs produced by PRP‐augmented IMR was only slightly higher than that produced by IMR with an MVP. As a result, neither treatment dominated over the other. However, because the ICER of PRP‐augmented IMR fell well above the $50,000 willingness‐to‐pay threshold, IMR with an MVP was determined to be the overall cost‐effective treatment strategy in the setting of young adult patients with isolated meniscal tears. Level of Evidence Level III, economic and decision analysis.

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.

AbstractStem 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.

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.