Search

Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular ("AI/machine learning"), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered.Cite this article: Bone Joint J  2022;104-B(12):1292-1303.

There exists great hope and hype in the literature surrounding applications of artificial intelligence (AI) to orthopaedic surgery. Between 2018 and 2021, a total of 178 AI-related articles were published in orthopaedics. However, for every 2 original research papers that apply AI to orthopaedics, a commentary or review is published (30.3%). AI-related research in orthopaedics frequently fails to provide use cases that offer the uninitiated an opportunity to appraise the importance of AI by studying meaningful questions, evaluating unknown hypotheses, or analyzing quality data. The hype perpetuates a feed-forward cycle that relegates AI to a meaningless buzzword by rewarding those with nascent understanding and rudimentary technical knowhow into committing several basic errors: (1) inappropriately conflating vernacular ("AI/machine learning"), (2) repackaging registry data, (3) prematurely releasing internally validated algorithms, (4) overstating the "black box phenomenon" by failing to provide weighted analysis, (5) claiming to evaluate AI rather than the data itself, and (6) withholding full model architecture code. Relevant AI-specific guidelines are forthcoming, but forced application of the original Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines designed for regression analyses is irrelevant and misleading. To safeguard meaningful use, AI-related research efforts in orthopaedics should be (1) directed toward administrative support over clinical evaluation and management, (2) require the use of the advanced model, and (3) answer a question that was previously unknown, unanswered, or unquantifiable., (Copyright © 2022 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.)

Artificial intelligence (AI) represents the fourth industrial revolution and the next frontier in medicine poised to transform the field of orthopaedics and sports medicine, though widespread understanding of the fundamental principles and adoption of applications remain nascent. Recent research efforts into implementation of AI in the field of orthopaedic surgery and sports medicine have demonstrated great promise in predicting athlete injury risk, interpreting advanced imaging, evaluating patient-reported outcomes, reporting value-based metrics, and augmenting the patient experience. Not unlike the recent emphasis thrust upon physicians to understand the business of medicine, the future practice of sports medicine specialists will require a fundamental working knowledge of the strengths, limitations, and applications of AI-based tools. With appreciation, caution, and experience applying AI to sports medicine, the potential to automate tasks and improve data-driven insights may be realized to fundamentally improve patient care. In this Current Concepts review, we discuss the definitions, strengths, limitations, and applications of AI from the current literature as it relates to orthopaedic sports medicine.

Artificial intelligence (AI), along with its subset technology machine learning, has transformed numerous industries through newfound efficiencies and supportive decision-making. These technologies have similarly begun to find application within United States healthcare, particularly orthopaedics. Although these modalities have the potential to similarly transform health care, there exist limitations that must also be recognized and understood. Unfortunately, most clinicians do not have an understanding of the fundamentals of AI and therefore may have challenges in contextualizing its impact in modern healthcare. The purpose of this review was to provide an overview of the key concepts of AI and machine learning with the orthopaedic surgeon in mind. The review further highlights the potential benefits and limitations of AI, along with an overview of its applications, in orthopaedics., (Copyright © 2021 by the American Academy of Orthopaedic Surgeons.)

Introduction: Given the expansion of commercial and recreational space exploration, orthopaedic surgeons will need to understand the implications of microgravity on cartilaginous damage and to anticipate the resulting pathology from accelerated chondrolysis. The purpose of this systematic review is to evaluate the effects of space and microgravity on hip and knee articular cartilage, including its impact on joint mobility and functional status., Materials and Methods: A review of the current literature was performed utilizing the terms "joints," "joint mobility," "articular cartilage," "knee," "hip," "space," "microgravity," and "osteoarthritis" in PubMed and Google Scholar from 1990 to 2018, yielding a total of 1,400 citations following the removal of 500 duplicates. Following screening by eligibility criteria, five reports were included., Results: Dysregulation of osteogenesis and weakened structural integrity of hip and knee cartilage were demonstrated secondary to microgravity. Adequate cartilage repair requires Earth-like conditions as signified by a statistically significant increase in serum cartilage oligomeric matrix protein concentrations in astronauts. Reduced loading led to the degradation of knee ligaments and menisci which may pose a risk for subluxation or dislocation. Murine studies demonstrated decreased articular cartilage thickness in the medial femoral condyle and patella as assessed by ultrasound. Additionally, glycosaminoglycan levels in unloaded rats were lower than weight-bearing rats, with a concomitant increase in matrix metalloproteinase-13 protein, degrading collagen. Return to weight-bearing demonstrated partial recovery of cartilaginous degeneration., Conclusions: Space and associated microgravity conditions adversely impact articular cartilage as demonstrated in murine and human studies. The pathogenetic process occurs due to the mechanically responsive nature of cartilage, with an increase in cartilage metabolism in microgravity. There remains a marked paucity of literature regarding the gravitational force necessary for adequate cartilage survival and the impact of space-related radiation on cartilage repair. Additionally, further studies should assess pharmacologic interventions, such as recombinant human fibroblast growth factor to stimulate cartilaginous growth.

Background: With the transition toward a value-based care delivery model, an evidence-based approach to quantify the effect of procedural volume on outcomes and cost presents an opportunity to understand and optimize the delivery of lower extremity arthroplasty. Stratum-specific likelihood ratio (SSLR) analysis has been recently applied to define benchmarks which confer a significant advantage in value at the hospital or surgeon level., Materials and Methods: In this report, the role, statistical technique, and future applications of SSLR analysis are described with an example outlined for total hip arthroplasty (THA)., Results: SSLR analysis provides multiple significant value-based thresholds, providing an advantage over previous methods used to describe the effects of surgeon and hospital volume. These benchmarks have been developed for THA, total knee arthroplasty (TKA), hip fracture, and several other orthopaedic procedures. Current SSLR analyses are limited by the databases employed, and the study of a national database may provide more generalizable benchmarks, which may be applied by hospitals and orthopaedic residencies to define minimum competency thresholds., Conclusion: The use of SSLR analysis provides a pragmatic, data-driven approach to understanding and communicating the volume-value relationship in orthopaedic surgery, particularly lower-extremity arthroplasty.

Introduction: Orthopaedic surgery residency positions are highly sought after. The purpose of this survey study was to report the following components of the applicant experience: (1) the number of programs to which applicants applied and interviewed, (2) the performance criteria associated with receiving interviews, (3) the way applicants respond to e-mail interview offers, (4) the pre- and post-interview communication between applicants and programs, (5) the importance of interview day activities and the determinants of the applicant rank order list (ROL), and (6) the financial cost of the application process., Methods: An online survey was administered and entirely completed by a representative sample of 100 orthopaedic surgery residency applicants for the 2015 to 2016 cycle during the 3-week period between the last interview of the application season and the deadline for ROL certification. The survey included 45 questions: 7 for background, 7 for competitiveness, 15 for the interaction between applicants and programs, 15 for the importance of interview day experience and the determinants of the applicant ROL, and 1 for the cost of attending each interview., Results: Students applied to 83 ± 27 programs, received 17 ± 10 interviews, and attended 12 ± 5 interviews. Interview offers correlated with, in descending order, Alpha Omega Alpha status, Step 2 Clinical Knowledge, and Step 1. The mean time to reply of interview offer was 17 minutes, yet 25% of the applicants lost at least one interview despite having at least one other person monitor the applicant's e-mail account. Applicants and programs frequently contacted each other to express interest. Although evaluating current residents was the most valuable aspect of interview day to applicants, the strongest determinants for applicants' ROLs were location and surgical experience, with research the least important factor. The cost of interview season was >$7,000 per applicant, excluding away externships., Conclusion: Applying to orthopaedic surgery residency is a complex, competitive, and costly experience for applicants. The application process may benefit from better expectation management of applicant candidacy and a more prohibitive communication policy between applicants and programs after the interview day.

Background: With the advent of the Consensus Core of Orthopedic Measures, arthroplasty surgeons are increasingly subjected to public performance reviews on physician-rating sites. Therefore, we evaluated (1) web site details of physician-rating sites, (2) differences between sites and the Consensus Core, (3) published patient experiences, (4) search rank among sites, and (5) differences between academic vs nonacademic and arthroplasty vs nonarthroplasty surgeons., Methods: The 5 busiest physician-rating sites were analyzed. To compare physician-rating sites to the Consensus Core, 3 reviewers analyzed the web site details. To evaluate patient ratings and reviews, orthopedists from the top 5 academic and nonacademic hospitals (2016 US News & World Report) were analyzed. Institution-produced rating sites were also analyzed. Findings were stratified between academic vs nonacademic and arthroplasty vs nonarthroplasty surgeons. Five hundred and six staff surgeons across 10 academic and nonacademic affiliated hospitals yielded 27,792 patient-generated ratings and reviews for 1404 accounts., Results: Features on all sites were practice location, languages spoken, and patient experience. Two sites autogenerated profiles of surgeons without consent. No physician-rating site contained all Consensus Core domains. The composite orthopedic surgeon rating was 4.1 of 5. No significant differences were found between academic and nonacademic affiliated surgeons. Arthroplasty surgeons had a greater number of reviews and ratings on 2 sites., Conclusion: Reliability of physician-rating sites is questionable, as none contained all Consensus Core domains. Autogeneration of surgeon profiles is occurring, and no differences between academic vs nonacademic or arthroplasty vs nonarthroplasty surgeons were found. Institution-produced sites may serve to better promote and market surgeons., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Background: There has been a rapid increase in research applying artificial intelligence (AI) to various subspecialties of orthopaedic surgery, including foot and ankle surgery. The purpose of this systematic review is to (1) characterize the topics and objectives of studies using AI in foot and ankle surgery, (2) evaluate the performance of their models, and (3) evaluate their validity (internal or external validation). Methods: A systematic literature review was conducted using PubMed/MEDLINE and Embase databases in December 2022. All studies that used AI or its subsets machine learning (ML) and deep learning (DL) in the setting of foot and ankle surgery relevant to orthopaedic surgeons were included. Studies were evaluated for their demographics, subject area, outcomes of interest, model(s) tested, model(s)' performance, and validity (internal or external). Results: A total of 31 studies met inclusion criteria: 14 studies investigated AI for image interpretation, 13 studies investigated AI for clinical predictions, and 4 studies were grouped as "other." Studies commonly explored AI for ankle fractures, calcaneus fractures, hallux valgus, Achilles tendon pathologies, plantar fasciitis, and sports injuries. For studies reporting the area under the receiver operating characteristic curve (AUC), AUCs ranged from 0.64 (poor) to 0.99 (excellent). Two studies (6.45%) reported external validation. Conclusion: Applications of AI in the field of foot and ankle surgery are expanding, particularly for image interpretation and clinical predictions. Current model performances range from poor to excellent, and most studies lack external validation, demonstrating a need for further research prior to deploying AI-based clinical applications. Level of Evidence: Level III, retrospective cohort study. [ABSTRACT FROM AUTHOR]