Who Are the Anatomic Outliers Undergoing Total Knee Arthroplasty? A Computed Tomography-Based Analysis of the Hip-Knee-Ankle Axis Across 1,352 Preoperative Computed Tomographies Using a Deep Learning and Computer Vision-Based Pipeline.

BACKGROUND: Dissatisfaction after total knee arthroplasty (TKA) ranges from 15 to 30%. While patient selection may be partially responsible, morphological and reconstructive challenges may be determinants. Preoperative computed tomography (CT) scans for TKA planning allow us to evaluate the hip-knee-ankle axis and establish a baseline phenotypic distribution across anatomic parameters. The purpose of this cross-sectional analysis was to establish the distributions of 27 parameters in a pre-TKA cohort and perform threshold analysis to identify anatomic outliers. METHODS: There were 1,352 pre-TKA CTs that were processed. A 2-step deep learning pipeline of classification and segmentation models identified landmark images and then generated contour representations. We used an open-source computer vision library to compute measurements for 27 anatomic metrics along the hip-knee axis. Normative distribution plots were established, and thresholds for the 15th percentile at both extremes were calculated. Metrics falling outside the central 70th percentile were considered outlier indices. A threshold analysis of outlier indices against the proportion of the cohort was performed. RESULTS: Significant variation exists in pre-TKA anatomy across 27 normally distributed metrics. Threshold analysis revealed a sigmoid function with a critical point at 9 outlier indices, representing 31.2% of subjects as anatomic outliers. Metrics with the greatest variation related to deformity (tibiofemoral angle, medial proximal tibial angle, lateral distal femoral angle), bony size (tibial width, anteroposterior femoral size, femoral head size, medial femoral condyle size), intraoperative landmarks (posterior tibial slope, transepicondylar and posterior condylar axes), and neglected rotational considerations (acetabular and femoral version, femoral torsion). CONCLUSIONS: In the largest non-industry database of pre-TKA CTs using a fully automated 3-stage deep learning and computer vision-based pipeline, marked anatomic variation exists. In the pursuit of understanding the dissatisfaction rate after TKA, acknowledging that 31% of patients represent anatomic outliers may help us better achieve anatomically personalized TKA, with or without adjunctive technology.

Modern Hip Arthroscopy for FAIS May Delay the Natural History of Osteoarthritis in 25% of Patients: A 12-Year Follow-up Analysis.

BACKGROUND: Little is known about the effect of modern hip arthroscopy on the natural history of femoroacetabular impingement syndrome (FAIS) with respect to joint preservation. PURPOSE: To (1) characterize the natural history of FAIS and (2) understand the effect of modern hip arthroscopy by radiographically comparing the hips of patients who underwent only unilateral primary hip arthroscopy with a minimum follow-up of 10 years. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: Between 2010 and 2012, 619 consecutive patients were reviewed from the practice of a single fellowship-trained hip arthroscopic surgeon. Inclusion criteria were FAIS, bilateral radiographic findings of femoroacetabular impingement, primary unilateral hip arthroscopy (labral repair, femoroplasty, or capsular closure), and minimum 10-year follow-up. The preoperative and minimum 10-year postoperative radiographs of patients were evaluated at each time point. Both operative and nonoperative hips were graded using the Tönnis classification or the presence of hip arthroplasty by 2 independent reviewers. Subgroup analyses were performed. RESULTS: A total of 200 hips from 100 patients were evaluated at a mean follow-up of 12.0 years. Preoperatively, 98% and 99% of operative and nonoperative hips were evaluated as Tönnis grades 0 and 1, respectively; 5% of nonoperative hips had worse Tönnis grades than operative hips. The nonoperative hip advanced to a worse Tönnis grade in 48% (48/100) of cases compared with 28% (28/100) among operative hips. At follow-up, Tönnis grades between hips were equal in 70% (70/100) of the cases, the operative hip had a better grade 25% (25/100) of the time, and the nonoperative hip had a better grade 5% (5/100) of the time. Modern hip arthroscopy was associated with a relative risk reduction of 42% in osteoarthritis progression. Impingement with borderline dysplasia, age, preoperative Tönnis grade, and alpha angle >65° were key risk factors in the radiographic progression of osteoarthritis. CONCLUSION: Although the majority of patients (70%) undergoing hip arthroscopy for FAIS did not experience differences between operative and nonoperative hips in terms of the radiographic progression of osteoarthritis, the natural history may be favorably altered for 25% of patients whose Tönnis grade was better after undergoing arthroscopic correction. Modern hip arthroscopy indications and techniques represent a valid joint-preservation procedure conferring a relative risk reduction of 42% in the progression of osteoarthritis. Arthroscopy for mixed patterns of impingement and instability were the fastest to degenerate.

The Fundamentals and Applications of Wearable Sensor Devices in Sports Medicine: A Scoping Review.

PURPOSE: To (1) characterize the various forms of wearable sensor devices (WSDs) and (2) review the peer-reviewed literature of applied wearable technology within sports medicine. METHODS: A systematic search of PubMed and EMBASE databases, from inception through 2023, was conducted to identify eligible studies using WSDs within sports medicine. Data extraction was performed of study demographics and sensor specifications. Included studies were categorized by application: athletic training, rehabilitation, and research. RESULTS: In total, 43 studies met criteria for inclusion in this review. Forms of WSDs include pedometers, accelerometers, encoders (consisting of magnetometers and gyroscopes), force sensors, global positioning system trackers, and inertial measurement units. Outcome metrics include step counts; gait, limb motion, and angular positioning; foot and skin pressure; change of direction and inclination, including analysis of both body parts and athletes on a field; displacement and velocity of body segments and joints; heart rate; plethysmography; sport-specific kinematics; range of motion, symmetry, and alignment; head impact; sleep; throwing biomechanics; and kinetic and spatiotemporal running metrics. WSDs are used in athletic training to assess sport-specific biomechanics and workload with a goal of injury prevention and training optimization, as well as for rehabilitation monitoring and research such as for risk predicting and aiding diagnosis. CONCLUSIONS: WSDs enable real-time monitoring of human performance across a variety of implementations and settings, allowing collection of metrics otherwise not achievable. WSDs are powerful tools with multiple applications within athletic training, patient rehabilitation, and orthopaedic and sports medicine research. CLINICAL RELEVANCE: Wearable technology may represent the missing link to quantitatively addressing return to play and previous performance. WSDs are commercially available and portable adjuncts that allow clinicians, trainers, and individual athletes to monitor biomechanical parameters, workload, and recovery status to better contextualize personalized training, injury risk, and rehabilitation.

Artificial intelligence-based applications in shoulder surgery leaves much to be desired: a systematic review.

Background: Artificial intelligence (AI) aims to simulate human intelligence using automated computer algorithms. There has been a rapid increase in research applying AI to various subspecialties of orthopedic surgery, including shoulder surgery. The purpose of this review is to assess the scope and validity of current clinical AI applications in shoulder surgery literature. Methods: A systematic literature review was conducted using PubMed for all articles published between January 1, 2010 and June 10, 2022. The search query used the terms as follows: (artificial intelligence OR machine learning OR deep learning) AND (shoulder OR shoulder surgery OR rotator cuff). All studies that examined AI application models in shoulder surgery were included and evaluated for model performance and validation (internal, external, or both). Results: A total of 45 studies were included in the final analysis. Eighteen studies involved shoulder arthroplasty, 13 rotator cuff, and 14 other areas. Studies applying AI to shoulder surgery primarily involved (1) automated imaging analysis including identifying rotator cuff tears and shoulder implants (2) risk prediction analyses including perioperative complications, functional outcomes, and patient satisfaction. Highest model performance area under the curve ranged from 0.681 (poor) to 1.00 (perfect). Only 2 studies reported external validation. Conclusion: Applications of AI in the field of shoulder surgery are expanding rapidly and offer patient-specific risk stratification for shared decision-making and process automation for resource preservation. However, model performance is modest and external validation remains to be demonstrated, suggesting increased scientific rigor is warranted prior to deploying AI-based clinical applications.

Exposure to Extended Reality and Artificial Intelligence-Based Manifestations: A Primer on the Future of Hip and Knee Arthroplasty.

BACKGROUND: Software-infused services, from robot-assisted and wearable technologies to artificial intelligence (AI)-laden analytics, continue to augment clinical orthopaedics - namely hip and knee arthroplasty. Extended reality (XR) tools, which encompass augmented reality, virtual reality, and mixed reality technology, represent a new frontier for expanding surgical horizons to maximize technical education, expertise, and execution. The purpose of this review is to critically detail and evaluate the recent developments surrounding XR in the field of hip and knee arthroplasty and to address potential future applications as they relate to AI. METHODS: In this narrative review surrounding XR, we discuss (1) definitions, (2) techniques, (3) studies, (4) current applications, and (5) future directions. We highlight XR subsets (augmented reality, virtual reality, and mixed reality) as they relate to AI in the increasingly digitized ecosystem within hip and knee arthroplasty. RESULTS: A narrative review of the XR orthopaedic ecosystem with respect to XR developments is summarized with specific emphasis on hip and knee arthroplasty. The XR as a tool for education, preoperative planning, and surgical execution is discussed with future applications dependent upon AI to potentially obviate the need for robotic assistance and preoperative advanced imaging without sacrificing accuracy. CONCLUSION: In a field where exposure is critical to clinical success, XR represents a novel stand-alone software-infused service that optimizes technical education, execution, and expertise but necessitates integration with AI and previously validated software solutions to offer opportunities that improve surgical precision with or without the use of robotics and computed tomography-based imaging.

Hip resurfacing arthroplasty as an alternative to total hip arthroplasty in patients aged under 40 years.

Aims: The aims of the study were to report for a cohort aged younger than 40 years: 1) indications for HRA; 2) patient-reported outcomes in terms of the modified Harris Hip Score (HHS); 3) dislocation rate; and 4) revision rate. Methods: This retrospective analysis identified 267 hips from 224 patients who underwent an hip resurfacing arthroplasty (HRA) from a single fellowship-trained surgeon using the direct lateral approach between 2007 and 2019. Inclusion criteria was minimum two-year follow-up, and age younger than 40 years. Patients were followed using a prospectively maintained institutional database. Results: A total of 217 hips (81%) were included for follow-up analysis at a mean of 3.8 years. Of the 23 females who underwent HRA, none were revised, and the median head size was 46 mm (compared to 50 mm for males). The most common indication for HRA was femoroacetabular impingement syndrome (n = 133), and avascular necrosis ( (n = 53). Mean postoperative HHS was 100 at two and five years. No dislocations occurred. A total of four hips (1.8%) required reoperation for resection of heterotopic ossification, removal of components for infection, and subsidence with loosening. The overall revision rate was 0.9%. Conclusion: For younger patients with higher functional expectations and increased lifetime risk for revision, HRA is an excellent bone preserving intervention carrying low complication rates, revision rates, and excellent patient outcomes without lifetime restrictions allowing these patients to return to activity and sport. Thus, in younger male patients with end-stage hip disease and higher demands, referral to a high-volume HRA surgeon should be considered.

Patient-Specific Safe Zones for Acetabular Component Positioning in Total Hip Arthroplasty: Mathematically Accounting for Spinopelvic Biomechanics.

BACKGROUND: Despite a growing understanding of spinopelvic biomechanics in total hip arthroplasty (THA), there is no validated approach for executing patient-specific acetabular component positioning. The purpose of this study was to (1) validate quantitative, patient-specific acetabular "safe zone" component positioning from spinopelvic parameters and (2) characterize differences between quantitative patient-specific acetabular targets and qualitative hip-spine classification targets. METHODS: From 2,457 consecutive primary THA patients, 22 (0.88%) underwent revision for instability. Spinopelvic parameters were measured prior to index THA. Acetabular position was measured following index and revision arthroplasty. Using a mathematical proof, we developed an open-source tool translating a surgeon-selected, preoperative standing acetabular target to a patient-specific safe zone intraoperative acetabular target. Difference between the patient-specific safe zone and the actual component position was compared before and after revision. Hip-spine classification targets were compared to patient-specific safe zone targets. RESULTS: Of the 22 who underwent revision, none dislocated at follow-up (4.6 [range, 1 to 6.9]). Patient-specific safe zone targets differed from prerevision acetabular component position by 9.1 ± 4.2° inclination/13.3 ± 6.7° version; after revision, the mean difference was 3.2 ± 3.0° inclination/5.3 ± 2.7° version. Differences between patient-specific safe zones and the median and extremes of recommended hip-spine classification targets were 2.2 ± 1.9° inclination/5.6 ± 3.7° version and 3.0 ± 2.3° inclination/7.9 ± 3.5° version, respectively. CONCLUSION: A mathematically derived, patient-specific approach accommodating spinopelvic biomechanics for acetabular component positioning was validated by approximating revised, now-stable hips within 5° version and 3° inclination. These patient-specific safe zones augment the hip-spine classification with prescriptive quantitative targets for nuanced preoperative planning.

Artificial Intelligence for Automated Implant Identification in Knee Arthroplasty: A Multicenter External Validation Study Exceeding 3.5 Million Plain Radiographs.

BACKGROUND: Surgical management of complications following knee arthroplasty demands accurate and timely identification of implant manufacturer and model. Automated image processing using deep machine learning has been previously developed and internally validated; however, external validation is essential prior to scaling clinical implementation for generalizability. METHODS: We trained, validated, and externally tested a deep learning system to classify knee arthroplasty systems as one of the 9 models from 4 manufacturers derived from 4,724 original, retrospectively collected anteroposterior plain knee radiographs across 3 academic referral centers. From these radiographs, 3,568 were used for training, 412 for validation, and 744 for external testing. Augmentation was applied to the training set (n = 3,568,000) to increase model robustness. Performance was determined by the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Implant identification processing speed was calculated. The training and testing sets were drawn from statistically different populations of implants (P < .001). RESULTS: After 1,000 training epochs by the deep learning system, the system discriminated 9 implant models with a mean area under the receiver operating characteristic curve of 0.989, accuracy of 97.4%, sensitivity of 89.2%, and specificity of 99.0% in the external testing dataset of 744 anteroposterior radiographs. The software classified implants at a mean speed of 0.02 seconds per image. CONCLUSION: An artificial intelligence-based software for identifying knee arthroplasty implants demonstrated excellent internal and external validation. Although continued surveillance is necessary with implant library expansion, this software represents a responsible and meaningful clinical application of artificial intelligence with immediate potential to globally scale and assist in preoperative planning prior to revision knee arthroplasty.

Editorial Commentary: Machine Learning Is Just a Statistical Technique, Not a Mystical Methodology or Peer Review Panacea.

Orthopaedic and sports medicine research surrounding artificial intelligence (AI) has dramatically risen over the last 4 years. Meaningful application and methodologic rigor in the scientific literature are critical to ensure appropriate use of AI. Common but critical errors for those engaging in AI-related research include failure to 1) ensure the question is important and previously unknown or unanswered; 2) establish that AI is necessary to answer the question; and 3) recognize model performance is more commonly a reflection of the data than the AI itself. We must take care to ensure we are not repackaging and internally validating registry data. Instead, we should be critically appraising our data-not the AI-based statistical technique. Without appropriate guardrails surrounding the use of artificial intelligence in Orthopaedic research, there is a risk of repackaging registry data and low-quality research in a recursive peer-reviewed loop.

Advancements in Artificial Intelligence for Foot and Ankle Surgery: A Systematic Review.

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.

Evaluating the Need for Preoperative MRI Before Primary Hip Arthroscopy in Patients 40 Years and Younger With Femoroacetabular Impingement Syndrome: A Multicenter Comparative Analysis.

Background: Routine hip magnetic resonance imaging (MRI) before arthroscopy for patients with femoroacetabular impingement syndrome (FAIS) offers questionable clinical benefit, delays surgery, and wastes resources. Purpose: To assess the clinical utility of preoperative hip MRI for patients aged ≤40 years who were undergoing primary hip arthroscopy and who had a history, physical examination findings, and radiographs concordant with FAIS. Study Design: Cohort study; Level of evidence, 3. Methods: Included were 1391 patients (mean age, 25.8 years; 63% female; mean body mass index, 25.6) who underwent hip arthroscopy between August 2015 and December 2021 by 1 of 4 fellowship-trained hip surgeons from 4 referral centers. Inclusion criteria were FAIS, primary surgery, and age ≤40 years. Exclusion criteria were MRI contraindication, reattempt of nonoperative management, and concomitant periacetabular osteotomy. Patients were stratified into those who were evaluated with preoperative MRI versus those without MRI. Those without MRI received an MRI before surgery without deviation from the established surgical plan. All preoperative MRI scans were compared with the office evaluation and intraoperative findings to assess agreement. Time from office to arthroscopy and/or MRI was recorded. MRI costs were calculated. Results: Of the study patients, 322 were not evaluated with MRI and 1069 were. MRI did not alter surgical or interoperative plans. Both groups had MRI findings demonstrating anterosuperior labral tears treated intraoperatively (99.8% repair, 0.2% debridement, and 0% reconstruction). Compared with patients who were evaluated with MRI and waited 63.0 ± 34.6 days, patients who were not evaluated with MRI underwent surgery 6.5 ± 18.7 days after preoperative MRI. MRI delayed surgery by 24.0 ± 5.3 days and cost a mean $2262 per patient. Conclusion: Preoperative MRI did not alter indications for primary hip arthroscopy in patients aged ≤40 years with a history, physical examination findings, and radiographs concordant with FAIS. Rather, MRI delayed surgery and wasted resources. Routine hip MRI acquisition for the younger population with primary FAIS with a typical presentation should be challenged.

Risk Factors for Failure After Osteochondral Allograft Transplantation of the Knee: A Systematic Review and Exploratory Meta-analysis.

BACKGROUND: Graft failure after osteochondral allograft transplantation (OCA) of the knee is a devastating outcome, often necessitating subsequent interventions. A comprehensive understanding of the risk factors for failure after OCA of the knee may provide enhanced prognostic data for the knee surgeon and facilitate more informed shared decision-making discussions before surgery. PURPOSE: To perform a systematic review and meta-analysis of risk factors associated with graft failure after OCA of the knee. STUDY DESIGN: Systematic review and meta-analysis; Level of evidence, 4. METHODS: The PubMed, Ovid/MEDLINE, and Cochrane databases were queried in April 2021. Data pertaining to study characteristics and risk factors associated with failure after OCA were recorded. DerSimonian-Laird binary random-effects models were constructed to quantitatively evaluate the association between risk factors and graft failure by generating effect estimates in the form of odds ratios (ORs) with 95% CIs, while mean differences (MDs) were calculated for continuous data. Qualitative analysis was performed to describe risk factors that were variably reported. RESULTS: A total of 16 studies consisting of 1401 patients were included. The overall pooled prevalence of failure was 18.9% (range, 10%-46%). There were 44 risk factors identified, of which 9 were explored quantitatively. There was strong evidence to support that the presence of bipolar chondral defects (OR, 4.20 [95% CI, 1.17-15.08]; P = .028) and male sex (OR, 2.04 [95% CI, 1.17-3.55]; P = .012) were significant risk factors for failure after OCA. Older age (MD, 5.06 years [95% CI, 1.44-8.70]; P = .006) and greater body mass index (MD, 1.75 kg/m2 [95% CI, 0.48-3.03]; P = .007) at the time of surgery were also significant risk factors for failure after OCA. There was no statistically significant evidence to incontrovertibly support that concomitant procedures, chondral defect size, and defect location were associated with an increased risk of failure after OCA. CONCLUSION: Bipolar chondral defects, male sex, older age, and greater body mass index were significantly associated with an increased failure rate after OCA of the knee. No statistically significant evidence presently exists to support that chondral defect size and location or concomitant procedures are associated with an increased graft failure rate after OCA of the knee. Additional studies are needed to evaluate these associations.

Artificial Intelligence for Automated Implant Identification in Total Hip Arthroplasty: A Multicenter External Validation Study Exceeding Two Million Plain Radiographs.

BACKGROUND: The surgical management of complications after total hip arthroplasty (THA) necessitates accurate identification of the femoral implant manufacturer and model. Automated image processing using deep learning has been previously developed and internally validated; however, external validation is necessary prior to responsible application of artificial intelligence (AI)-based technologies. METHODS: We trained, validated, and externally tested a deep learning system to classify femoral-sided THA implants as one of the 8 models from 2 manufacturers derived from 2,954 original, deidentified, retrospectively collected anteroposterior plain radiographs across 3 academic referral centers and 13 surgeons. From these radiographs, 2,117 were used for training, 249 for validation, and 588 for external testing. Augmentation was applied to the training set (n = 2,117,000) to increase model robustness. Performance was evaluated by area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Implant identification processing speed was calculated. RESULTS: The training and testing sets were drawn from statistically different populations of implants (P < .001). After 1,000 training epochs by the deep learning system, the system discriminated 8 implant models with a mean area under the receiver operating characteristic curve of 0.991, accuracy of 97.9%, sensitivity of 88.6%, and specificity of 98.9% in the external testing dataset of 588 anteroposterior radiographs. The software classified implants at a mean speed of 0.02 seconds per image. CONCLUSION: An AI-based software demonstrated excellent internal and external validation. Although continued surveillance is necessary with implant library expansion, this software represents responsible and meaningful clinical application of AI with immediate potential to globally scale and assist in preoperative planning prior to revision THA.

Concerns surrounding application of artificial intelligence in hip and knee arthroplasty : a review of literature and recommendations for meaningful adoption.

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.

The development and deployment of machine learning models.

Applications of artificial intelligence, specifically machine learning, are becoming increasingly popular in Orthopaedic Surgery, and medicine as a whole. This growing interest is shared by data scientists and physicians alike. However, there is an asymmetry of understanding of the developmental process and potential applications of machine learning. As new technology will undoubtedly affect clinical practice in the coming years, it is important for physicians to understand how these processes work. The purpose of this paper is to provide clarity and a general framework for building and assessing machine learning models.

Meaningless Applications and Misguided Methodologies in Artificial Intelligence-Related Orthopaedic Research Propagates Hype Over Hope.

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.

Preoperative Magnetic Resonance Imaging Offers Questionable Clinical Utility, Delays Time to Hip Arthroscopy, and Lacks Cost-Effectiveness in Patients Aged ≤40 Years With Femoroacetabular Impingement Syndrome: A Retrospective 5-Year Analysis.

PURPOSE: To assess the clinical utility of preoperative magnetic resonance imaging (MRI) and quantify the delay in surgical care for patients aged ≤40 years undergoing primary hip arthroscopy with history, physical examination, and radiographs concordant with femoroacetabular impingement syndrome (FAIS). METHODS: From August 2015 to December 2020, 1,786 consecutive patients were reviewed from the practice of 1 fellowship-trained hip arthroscopist. Inclusion criteria were FAIS, primary surgery, and age ≤40 years. Exclusion criteria were MRI contraindication, reattempt of conservative management, or concomitant periacetabular osteotomy. After nonoperative treatment options were exhausted and a surgical plan was established, patients were stratified by those who presented with versus without MRI. Those without existing MRI received one, and any deviations from the surgical plan were noted. All preoperative MRIs were compared with office evaluation and intraoperative findings to assess agreement. Demographic data, Hip Disability and Osteoarthritis Outcome Score (HOOS)-Pain, and time from office to MRI or arthroscopy were recorded. RESULTS: Of the patients indicated by history, physical examination, and radiographs alone (70% female, body mass index 24.8 kg/m2, age 25.9 years), 198 patients presented without MRI and 934 with MRI. None of the 198 had surgical plans altered after MRI. Patients in both groups had MRI findings demonstrating anterosuperior labral tears that were visualized and repaired intraoperatively. Mean time from office to arthroscopy for patients without MRI versus those with was 107.0 ± 67 and 85.0 ± 53 days, respectively (P < .001). Time to MRI was 22.8 days. No difference between groups was observed among the 85% of patients who surpassed the HOOS-Pain minimal clinically important difference (MCID). CONCLUSION: Once indicated for surgery based on history, physical examination, and radiographs, preoperative MRI did not alter the surgical plan for patients aged ≤40 years with FAIS undergoing primary hip arthroscopy. Moreover, preoperative MRI delayed time to arthroscopy. The necessity of routine preoperative MRI in the young primary FAIS population should be challenged.

Psoriasis and Post-Surgical Infections in Primary Total Knee Arthroplasty: An Analysis of 10,727 Patients.

BACKGROUND: Psoriasis is a dermatologic condition characterized by erythematous plaques that may increase wound complications and deep infections following total knee arthroplasty (TKA). There is a paucity of evidence concerning the association of this disease and complications after TKA. This study aimed to determine if patients who have psoriasis vs non-psoriatic patients have differences in demographics and various comorbidities as well as post-operative infections, specifically the following: (1) wound complications; (2) cellulitic episodes; and (3) deep surgical site infections (SSIs). METHODS: We identified 10,727 patients undergoing primary TKA utilizing an institutional database between January 1, 2017 and April 1, 2019. A total of 133 patients who had psoriasis (1.2%) were identified using International Classification of Diseases, Tenth Revision codes and compared to non-psoriatic patients. The rate of wound complications, cellulitic episodes, and deep SSIs were determined. After controlling for age and various comorbidities, multivariate analyses were performed to identify the associated risks for post-operative infections. RESULTS: Psoriasis patients showed an increased associated risk of deep SSIs (3.8%) compared to non-psoriasis patients (1.2%, P = .023). Multivariate analyses demonstrated a significant associated risk of deep SSIs (odds ratio 7.04, 95% confidence interval 2.38-20.9, P < .001) and wound complications (odds ratio 4.44, 95% confidence interval 1.02-19.2, P = .047). CONCLUSION: Psoriasis is an inflammatory dermatologic condition that warrants increased pre-operative counseling, shared decision-making, and infectious precautions in the TKA population given the increased risk of wound complications and deep SSIs. Increased vigilance is required given the coexistence of certain comorbidities with this population, including depression, substance use disorder, smoking history, gastroesophageal reflux disease, and inflammatory bowel disease.

Time-Driven Activity-Based Costing Accurately Determines Bundle Cost for Rotator Cuff Repair.

PURPOSE: The purpose of this study was to determine the cost of the episode of care for primary rotator cuff repair (RCR) from day of surgery to 90 days postoperatively using the time-driven activity-based costing (TDABC) method. The secondary purpose of this study was to identify the main drivers of cost for both phases of care. METHODS: This retrospective case series study used the TDABC method to determine the bundled cost of care for an RCR. First, a process map of the RCR episode of care was constructed in order to determine drivers of fixed (i.e., rent, power), direct variable (i.e., healthcare personnel), and indirect costs (i.e., marketing, building maintenance). The study was performed at a Midwestern tertiary care medical system, and patients were included in the study if they underwent an RCR from January 2018 to January 2019 with at least 90 days of postoperative follow-up. In this article, all costs were included, but we did not account for fees to provider and professional groups. RESULTS: The TDABC method calculated a cost of $10,569 for a bundled RCR, with 76% arising from the operative phase and 24% from the postoperative phase. The main driver of cost within the operative phase was the direct fixed costs, which accounted for 35% of the cost in this phase, and the largest contributor to cost within this category was the cost of implants, which accounted for 55%. In the postoperative phase of care, physical therapy visits were the greatest contributor to cost at 59%. CONCLUSION: In a bundled cost of care for RCR, the largest cost driver occurs on the day of surgery for direct fixed costs, in particular, the implant. Physical therapy represents over half of the costs of the episode of care. Better understanding the specific cost of care for RCR will facilitate optimization with appropriately designed payment models and policies that safeguard the interests of the patient, physician, and payer. LEVEL OF EVIDENCE: IV, therapeutic case series.