Kelly, Bryan T.

Background: Adequate cam resection during hip arthroscopy for femoroacetabular impingement syndrome (FAIS) is necessary to restore femoral head-neck offset while avoiding under- or over-resection. The relationship between the extent of femoroplasty and postoperative patient-reported outcomes remains unclear. Purpose: We sought to answer the following questions: What is the association between the extent of cam femoroplasty resection and postoperative improvements? and is a 5% resection threshold associated with improved outcomes? Methods: We conducted a retrospective review of patients with adequate acetabular coverage who underwent primary hip arthroscopy with femoroplasty between January 2015 and December 2022 at our institution and had preoperative and 1- and/or 2-year postoperative modified Harris hip score (mHHS) and/or International Hip Outcome Tool (iHOT) scores. We included 400 patients (409 hips; ages 14–50 years). Outcomes included score changes and achievement of minimum clinically important difference (MCID) and substantial clinical benefit (SCB). Postoperative Dunn lateral radiographs quantified femoroplasty by resection depth percentage (resection depth/femoral head diameter), proximal resection angle, and postoperative α angle. Hips were classified as under-resection, neutral (0%–5%), or over-resection (>5%), and differences in outcomes were compared between resection groups. Multivariable regression analyses controlled for demographic, radiographic, and surgical variables. Results: Mean resection depth was 4.8%; mean proximal resection angle was 72.6°. No differences were observed in score improvements or MCID/SCB rates between groups with under-resection versus over-resection at 1 or 2 years. Greater resection depth was associated with larger 1-year mHHS and iHOT improvements and independently predicted 1-year iHOT MCID and SCB. Higher proximal resection angle predicted 2-year iHOT MCID. Conclusion: In this retrospective, single-institution cohort study, we found that in patients with FAIS, greater femoroplasty depth was associated with improved 1-year clinical outcomes. Level of Evidence: Level III, retrospective cohort study.

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 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.

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.

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.

Purpose To evaluate the association of preoperative intra‐articular hip injection with surgical site infection after hip arthroscopy. Methods A large administrative database was used to identify all patients undergoing hip arthroscopy from 2007 to 2015 within a single private insurer and from 2005 to 2012 within Medicare in the United States. Those that received an ipsilateral preoperative intra‐articular hip injection were identified. The patients were then divided into the following groups based on the interval between preoperative injection and ipsilateral hip arthroscopy: (1) <3 months, (2) 3 to 6 months, and (3) 6 to 12 months. These groups were compared to a control group composed of patients with no history or a remote history (>12 months) of preoperative hip injection. Patients developing a surgical site infection within 6 months following hip arthroscopy were identified using International Classification of Diseases, Ninth Revision, and Current Procedural Terminology codes associated with infection. Groups were compared using a multivariate logistic regression analysis to control for age, gender, body mass index, smoking status, alcohol usage, and multiple medical comorbidities including diabetes mellitus, hemodialysis use, inflammatory arthritis, and peripheral vascular disease. Results In total, 19% of privately insured and 6% of Medicare patients received a hip injection within 12 months of hip arthroscopy. The overall infection rate in privately insured and Medicare patients was 1.19% and 1.10%, respectively. Preoperative hip injection within 3 months of surgery was associated with a significantly higher risk of postoperative infection versus controls (2.16%, odds ratio [OR] 6.1, P < .001, for privately insured group; 2.80%, OR 1.99, P = .037, for Medicare group). In contrast, preoperative hip injection given after more than 3 months of surgery was not associated with an increased risk of postoperative infection versus controls. Conclusions Risk of infection after hip arthroscopy increased when preoperative intra‐articular hip injections were given within 3 months of surgery. Level of Evidence Level III, retrospective comparative study.