Artificial intelligence (AI) and its subset, machine learning (ML), have significantly impacted clinical medicine, particularly in knee arthroplasty (KA). These technologies utilize algorithms for tasks such as predictive analytics and image recognition, improving preoperative planning, intraoperative navigation, and postoperative complication anticipation. This systematic review presents AI-driven tools' clinical implications in total and unicompartmental KA, focusing on enhancing patient outcomes and operational efficiency.

A systematic search was conducted across multiple databases including Cochrane Central Register of Controlled Trials, Embase, OVID Medline, PubMed, and Web of Science, following the PRISMA guidelines for studies published in the English language till March 2024. Inclusion criteria targeted adult human models without geographical restrictions, specifically related to total or unicompartmental KA.

A total of 153 relevant studies were identified, covering various aspects of ML application for KA. Topics of studies included imaging modalities (n = 28), postoperative primary KA complications (n = 26), inpatient status (length of stay, readmissions, and cost) (n = 24), implant configuration (n = 14), revision (n = 12), patient-reported outcome measures (PROMs) (n = 11), function (n = 11), procedural communication (n = 8), total knee arthroplasty/unicompartmental knee arthroplasty prediction (n = 6), outpatient status (n = 4), perioperative efficiency (n = 4), patient satisfaction (n = 3), opioid usage (n = 3). A total of 66 ML models were described, with 48.7% of studies using multiple approaches.

This review assesses ML applications in knee arthroplasty, highlighting their potential to improve patient outcomes. While current algorithms and AI show promise, our findings suggest areas for enhancement in predictive performance before widespread clinical adoption.

Paediatric spine deformity surgery is a high-stakes procedure. It demands the surgeon to have exceptional anatomical knowledge and precise visuospatial awareness. There is increasing demand for precision medicine, which rapid advancements in computational technologies have made possible with the recent explosion of AI and machine learning (ML). We present the surgical and ethical applications of AI and ML in diagnosis, prognosis, image processing, and outcomes in the field of paediatric spine deformity.

Artificial intelligence (AI), with its technologies such as machine perception, robotics, natural language processing, expert systems, and machine learning (ML) with its subset deep learning, have transformed patient care and administration in all fields of modern medicine. For many clinicians, however, the nature, scope, and resulting possibilities of ML and deep learning might not yet be fully clear. This narrative review provides an overview of the application of ML and deep learning in musculoskeletal medicine. It first introduces the concept of AI and machine learning and its associated fields. Different machine concepts such as supervised, unsupervised and reinforcement learning will then be presented with current applications and clinical perspective. Finally deep learning applications will be discussed. With significant improvements over the last decade, ML and its subset deep learning today offer potent tools for numerous applications to implement in clinical practice. While initial setup costs are high, these investments can reduce workload and cost globally. At the same time, many challenges remain, such as standardisation in data labelling and often insufficient validity of the obtained results. In addition, legal aspects still will have to be clarified. Until good analyses and predictions are obtained by an ML tool, patience in training and suitable data sets are required. Awareness of the strengths of ML and the limitations that lie within it will help put this technique to good use.

Revision total knee arthroplasty (rTKA) and revision total hip arthroplasty (rTHA) are among the most resource-intensive orthopaedic procedures. The primary aim of this study was to compare the accuracy of machine learning (ML) models between administrative and institutional datasets for predicting duration of surgery (DOS), length of stay (LOS), and 30-day hospital readmission for rTKA and rTHA based on preoperative factors and identify significant predictive features.

A national quality improvement database was queried from 2014 to 2019, and a local institutional arthroplasty database was queried from 2012 to 2022 for rTKAs and rTHAs. Datasets were independently split into training, validation, and testing and normalized based on year. Artificial neural networks (ANNs) for both procedures and each outcome were created, and their performance was compared to multivariable regression models. Models were compared between datasets using buffer accuracy (BA). Feature importance of ANNs was retrieved using Shapley Additive exPlanations (SHAP) values.

A total of 22,851 and 1,025 rTKA and 14,262 and 703 rTHA patients were included from the national and institutional datasets, respectively. For DOS, the institutional ANNs outperformed the national ANNs with a 76.2 versus 45.4%, and 55.4 versus 43.1% 30-minute BA for rTKA and rTHA, respectively. For LOS, the national ANNs yielded a superior 2-day BA than the institutional ANNs of 81.8 versus 67.7%, and 71.4 versus 48.2% for rTKA and rTHA, respectively. The 30-day readmission prediction using the national database had area under the curve (AUC) scores of 0.593 and 0.590 for rTKA and rTHA, respectively.

The performance of ANN models varied significantly by dataset, but all models were superior to using historic averages. Future work should consider using accurate, arthroplasty-specific datasets based on the important features identified.

Many new technologies focused mainly on improving surgical accuracy were first developed in total knee arthroplasty and have not yet shown significant value. These non-significant clinical improvements could potentially be explained by an inadequate target. In this current concept paper, the authors will describe how artificial intelligence (AI), robotic-assisted surgery, and custom implants allow the definition of new targets and the standardization of the TKA process. As paradoxical as it may be, new technologies in TKA will allow for better standardization in the overall way in which this procedure is carried out. Achieving this goal can be accomplished by incorporating AI-driven tools into the medical field. These tools are intended to enhance decision making, refine surgical planning, and increase the precision and consistency of surgical procedures. Moreover, custom implants with personalized alignment, beyond restoring native anatomy, will define new targets and standardize the whole TKA process.

Accurate preoperative planning in total knee arthroplasty (TKA) is essential. Traditional manual radiographic planning can be time-consuming and potentially prone to inaccuracies. This study investigates the performance of an AI-based radiographic planning tool in comparison with manual measurements in patients undergoing total knee arthroplasty, using a retrospective observational design to assess reliability and efficiency.

We retrospectively compared the Autoplan tool integrated within the mediCAD software (mediCAD Hectec GmbH, Altdorf, Germany), routinely implemented in our institutional workflow, to manual measurements performed by two orthopedic specialists on pre- and postoperative radiographs of 100 patients who underwent elective TKA. The following parameters were measured: leg length, mechanical axis deviation (MAD), mechanical lateral proximal femoral angle (mLPFA), anatomical mechanical angle (AMA), mechanical lateral distal femoral angle (mLDFA), joint line convergence angle (JLCA), mechanical medial proximal tibial angle (mMPTA), and mechanical tibiofemoral angle (mTFA).Intraclass correlation coefficients (ICCs) were calculated to assess measurement reliability, and the time required for each method was recorded.

The Autoplan tool demonstrated high reliability (ICC > 0.90) compared with manual measurements for linear parameters (e.g., leg length and MAD). However, the angular measurements of mLPFA, JLCA, and AMA exhibited poor reliability (ICC < 0.50) among all raters. The Autoplan tool significantly reduced the time required for measurements compared to manual measurements, with a mean time saving of 44.3 seconds per case (95 % CI: 43.5-45.1 seconds, p < 0.001).

AI-assisted tools like the Autoplan tool in mediCAD offer substantial time savings and demonstrate reliable measurements for certain linear parameters in preoperative TKA planning. However, the observed low reliability in some measurements, even amongst experienced human raters, suggests inherent challenges in the radiographic assessment of angular parameters. Further development is needed to improve the accuracy of automated angular measurements, and to address the inherent variability in their assessment.

To assess the inter- and intrarater reliability of 21 anatomical landmarks initially placed with an artificial intelligence algorithm and then manually verified with human input.

Thirty computed tomography scans of the knees of participants from the Multicenter Osteoarthritis Study (MOST) ages 45 to 55 years were included. Approximately one-half experienced progression of patellofemoral osteoarthritis, defined as an increased cartilage score in the patellofemoral compartment on magnetic resonance imaging over 2 years. The algorithm automatically placed 19 anatomic landmarks on the femur, tibia, and patella. An additional 2 landmarks were added manually. Two landmark reviewers separately reviewed all 30 scans and verified all landmarks. After 2 weeks, one reviewer repeated the process for the same dataset. The mean Euclidean distance of manual landmark displacement, mean absolute disagreement between and within raters, and intraclass correlation coefficients for inter- and intrarater reliability were calculated.

All landmarks had excellent inter-rater reliability. The tibial and femoral shaft centers had intraclass correlation coefficients (ICCs) of 1, indicating their positions did not differ. Seventeen landmarks had ICCs between 0.90 and 0.99 and the tibial tuberosity had an ICC of 0.87. Intrarater reliability scores were 1 for 16 landmarks and between 0.90 and 0.99 for the remaining 5.

There was excellent agreement on the locations of all 21 landmarks evaluated in this study.

The potential role of artificial intelligence in medical imaging and orthopaedic research is a growing area of interest. The excellent reliability demonstrated across multiple landmarks in our study reveals the potential for semiautomated 3-dimensional methods to enhance precision of anatomical measurements of the knee over 2-dimensional methods.

Total knee arthroplasty (TKA) is considered the gold standard treatment for end-stage knee osteoarthritis. Common complications associated with TKA include implant loosening and periprosthetic fractures, which often require revision surgery or fixation. Challenges arise when medical records related to the knee prosthesis are lost, making it difficult to plan for revision surgery effectively. This study aims to develop an artificial intelligence (AI) system to classify the types of knee prosthetic implants using plain radiographs.

This retrospective experimental study includes seven types of knee prostheses commonly used in our hospital. The artificial intelligence (AI) system was trained using YOLO (You Only Look Once) version 9, utilizing a dataset of 3228 post-operative and follow-up knee arthroplasty X-ray images. The plain radiographic images were augmented, resulting in a dataset of 25,800 images. Model parameters were fine-tuned to optimize performance for implant classification.

The mean age of the patients was 62.8 years. Right knee arthroplasty was performed in 48.3% of cases, while left knee arthroplasty was performed in 51.7%. The images of knee prostheses comprised 50.9% of the dataset from the anteroposterior (AP) view and 49.1% from the lateral view. The AI model demonstrated exceptional performance metrics, achieving precision, recall, and accuracy rates of 100%, with an F1 score of 1. Additionally, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated to be 100%.

This AI model successfully classifies knee prosthetic implants from plain radiographs. This capability serves as a valuable tool for surgeons, enabling precise planning for revision surgeries and periprosthetic fracture fixation surgery, ultimately contributing to improved patient outcomes. The high accuracy achieved by the AI underscores its potential to enhance surgical efficiency and effectiveness in managing knee arthroplasty complications.

Artificial intelligence is very likely to be a pioneering technology in arthroplasty, with a wide range of pre-, intra- and post-operative applications. The opportunities for patients, doctors and healthcare policy are considerable, especially in the context of optimized and individualized patient care.

Despite these diverse possibilities, there are currently only a few AI applications in routine clinical practice, mainly due to the limited availability of analyzable health data. AI systems are only as good as the data they are trained with. If the data is insufficient, incomplete or biased, the AI may draw false conclusions. The current results of such AI applications in arthroplasty must, therefore, be viewed critically, especially as previous data bases were not designed a priori for AI applications.

The successful integration of AI, therefore, requires a targeted focus on the development of a specific data structure. In order to exploit the full potential of AI, comprehensive clinical data volumes are required, which can only be realized through a multicentric approach. In this context, ethical and data protection issues remain a further question, and not only in orthopaedics. Cooperative efforts at national and international levels are, therefore, essential in order to research and develop new AI applications.

In the landscape of knee-related health issues there has been a notable shift in treatment protocols. Nowadays, there is a growing trend toward primary total knee arthroplasty (TKA) in the event of periarticular knee fractures. A review of the literature on TKA in acute knee fractures has been done in order to provide scientific evidence to the four statements submitted for voting to the members of the European Knee Society (EKS).

A literature review has been performed around four topics of TKA in acute knee fractures, specifically: 1) The indications for TKA in acute knee fractures are undoubtful and clear; 2) Pre-existing osteoarthritis is not mandatory for the indication of TKA in acute fractures, while age, co-morbidities and type of fracture are; 3) A series of established criteria with scores to give indication for TKA (approved algorithm) is needed; and 4) This (complex) surgery must be performed in referral centers with all technical options and specific peri-operative management and post-operative care.

The panel of experts therefore believes that the indications cannot be considered undoubtful and clear. According to the literature up to the time of the consensus vote, there was no objective method for deciding on the treatment to offer the patient (Agree: 34.1%, Disagree: 61%, Abstain: 4.9%). It emerges that there are no mandatory conditions for the treatment of acute knee fractures with TKA (Agree: 32.3%, Disagree: 51.6%, Abstain: 16.1%). However, there are several characteristics to consider for a multifactorial evaluation rather than being limited to a single condition. While the consensus has highlighted a need for a scoring system to guide surgical decisions in periarticular knee fractures (Agree: 88.24%, Disagree: 8.82%, Abstain: 2.94%), research in the literature has confirmed that, to date, no validated algorithm exists. After the vote, a score was proposed, which requires validation. Although the panel of experts does not deem it necessary for this surgery to be reserved for reference centers (Agree: 32.35%, Disagree: 50%, Abstain: 17.65%), literature suggests that it is crucial that before undertaking knee arthroplasty in the setting of an acute fracture around the knee, the orthopedic surgeon is confident with all the necessary skills for a complex intervention that requires advanced knowledge and practical competence in osteosynthesis and revision TKA.

This discussion on the questions voted by the panel of experts has allowed for an in-depth exploration of a topic of interest, assessing indications, contraindications, types of possible treatment, and the critical aspects to consider when treating an acute fracture around the knee with a prosthesis. It is important to consider that the choice must be carefully weighed, evaluating the risks and benefits, with an increasingly need for a scoring system for selecting the most appropriate treatment.

Periprosthetic joint infection (PJI) is a complication that occurs in less than 1% of patients after unicompartmental knee arthroplasty (UKA). Though infrequent, it may potentially lead to revision while placing a significant financial burden on the healthcare system. Preoperative, intra-operative, and postoperative strategies should be implemented to minimize the risk of PJI. Patient optimization prior to surgery can help to identify patients at risk for PJI and also maximize the health of the patient prior to surgery. Intraoperative and postoperative strategies can also mitigate the risk of postoperative infection. This article will summarize the evidence for preoperative, intra-operative, and postoperative strategies to prevent PJI in UKA. This will include topics on malnutrition and obesity, Staphylococcus aureus, smoking, human immunodeficiency virus, rheumatoid arthritis, as well as skin preparation, laminar air flow, preoperative antibiotic administration antimicrobial incision drapes, pulsatile lavage, vancomycin powder, wound closure method, thromboprophylactic agents, and closed incisional negative pressure wound therapy dressings.

Background: This research employs bibliometric and text-mining analysis to explore artificial intelligence (AI) advancements within surgical procedures. The growing significance of AI in healthcare underscores the need for healthcare managers to prioritize investments in this technology. Purpose: To assess the increasing impact of AI on surgical practices through a comprehensive analysis of scientific literature, providing insights that can guide managerial decision-making in adopting AI solutions.Research Design: The study analyzes over 6000 scientific articles published since 1990 to evaluate trends and contributions in the field, informing managers about the current landscape of AI in surgery.Study Sample: The research focuses on publications from various influential publishers across North America, Northern Asia, and Eastern & Western Europe, highlighting key markets for AI implementation in surgical settings.Data Collection and Analysis: A bibliometric approach was utilized to identify key contributors and influential journals. At the same time, text-mining techniques highlighted significant keywords related to AI in surgery, aiding managers in recognizing essential areas for further exploration and investment.Results: The year 2022 marked a significant upsurge in publications, indicating widespread AI integration in healthcare. The U.S. emerged as the foremost contributor, followed by China, the UK, Germany, Italy, the Netherlands, and India. Key journals, such as Annals of Surgery and Spine Journal, play a crucial role in disseminating research findings, serving as valuable resources for managers seeking to stay informed.Conclusions: The findings underscore AI's pivotal role in enhancing diagnostic precision, predicting treatment outcomes, and improving operational efficiency in surgical practices. This progress represents a significant milestone in modern medical science, paving the way for intelligent healthcare solutions and further advancements in the field. Healthcare managers should leverage these insights to foster innovation and improve patient care standards.

Total Knee Arthroplasty (TKA) is frequently performed for advanced osteoarthritis, with patient-reported outcome measures (PROMs) traditionally reporting on efficacy. These subjective evaluations, although useful, may inaccurately reflect post-TKA activity levels. With technological advancements, smart implantable devices (SIDs) offer objective, real-time gait metrics, potentially providing a more accurate postoperative recovery assessment. This study compares these objective metrics with PROMs to evaluate TKA success more effectively.

We conducted a retrospective cohort study with 88 participants undergoing TKA using a SID. Eligible patients were aged 18 years or older and had advanced osteoarthritis. We excluded those who had bilateral TKAs, joint infections, or neuromuscular disease. The SID system collected daily gait metrics, including step count, distance traveled, walking speed, stride length, cadence, and functional knee range of motion. The PROMs, including Knee Injury and Osteoarthritis Outcome Score-Joint Replacement, Veterans Rand 12 Physical Component Summary, and Veterans Rand 12 Mental Component Summary, were analyzed against SID gait metrics. Among the 88 patients, 80 provided continuous data over 12 weeks.

All gait metrics, except stride length, significantly increased at the 12-week point (P < .05). The PROMs also significantly improved postoperatively (P < .05). Initial low positive correlations between 12-week PROMs and SID metrics decreased after adjusting for demographic variables, leaving only weak correlations between the Veterans Rand 12 Physical Component Summary and Knee Injury and Osteoarthritis Outcome Score-Joint Replacement with functional knee range of motion (r = 0.389, P = .002; r = 0.311, P = .014, respectively), and Veterans Rand 12 Mental Component Summary with step count (r = 0.406, P = .001) and distance traveled (r = 0.376, P = .003).

This study indicates that both PROMs and SID gait metrics show significant improvements post-TKA, though they correlate weakly with each other, suggesting a possible discrepancy between perceived recovery and actual functional improvement. The SID gait metrics might provide a valuable addition to traditional PROMs by offering an objective representation of physical capabilities unaffected by patient compliance or subjective perceptions of recovery. Further research is needed to validate these findings in larger populations and to explore whether integrating SID metrics can enhance long-term functional outcomes.

Knee arthroplasty, commonly performed to treat osteoarthritis, necessitates precise surgical techniques for optimal outcomes. The introduction of systems such as the Persona Knee System (Zimmer Biomet, Warsaw, IN, USA) has revolutionized knee arthroplasty, promising enhanced precision and better patient outcomes. This study investigates the application of robotic planning specifically in knee prosthetic surgeries, with a focus on Persona Knee System prostheses. We conducted a retrospective analysis of 300 patients who underwent knee arthroplasty using the Persona Knee System between January 2020 and November 2023, including demographic data, surgical parameters, and preoperative imaging. Robotic planning was employed to simulate surgical procedures. The planning process integrated preoperative imaging data from a specific program adopted for conducting digital preoperative planning, and statistical analyses were conducted to assess correlations between patient characteristics and surgical outcomes. Out of 300 patients, 85% presented with minor deformities, validating the feasibility of robotic planning. Robotic planning demonstrated precise prediction of optimal arthroplasty sizes and alignment, closely aligning with preoperative imaging data. This study highlights the potential benefits of robotic planning in knee arthroplasty surgeries, particularly in cases with minor deformities. By leveraging preoperative imaging data and integrating advanced robotic technologies, surgeons can improve precision and efficacy in knee arthroplasty. Moreover, robotic technology allows for a reduced level of constraint in the intraoperative choice between Posterior-Stabilized and Constrained Posterior-Stabilized liners compared with an imageless navigated procedure.

The integration of artificial intelligence (AI) into orthopaedic care has gained considerable interest in recent years, evidenced by the growing body of literature boasting wide-ranging applications across the perioperative setting. This includes automated diagnostic imaging, clinical decision-making tools, optimisation of implant design, robotic surgery, and remote patient monitoring. Collectively, these advances propose to enhance patient care and improve system efficiency. Musculoskeletal pathologies represent the most significant contributor to global disability, with roughly 1.71 billion people afflicted, leading to an increasing volume of patients awaiting planned orthopaedic surgeries. This has exerted a considerable strain on healthcare systems globally, compounded by both the COVID-19 pandemic and the effects of an ageing population. Subsequently, patients face prolonged waiting times for surgery, with further deterioration and potentially poorer outcomes as a result. Furthermore, incorporating AI technologies into clinical practice could provide a means of addressing current and future service demands. This review aims to present a clear overview of AI applications across preoperative, intraoperative, and postoperative stages to elucidate its potential to transform planned orthopaedic care.

Orthopedic surgeons use manual measurements, acetate templating, and dedicated software to determine the appropriate implant size for total knee arthroplasty (TKA). This study aimed to use deep learning (DL) to assist in deciding the femoral and tibial implant sizes without manual manipulation and to evaluate the clinical validity of the DL decision by comparing it with conventional manual procedures.

Two types of DL were used to detect the femoral and tibial regions using the You Only Look Once algorithm model and to determine the implant size from the detected regions using convolutional neural network. An experienced surgeon predicted the implant size for 234 patient cases using manual procedures, and the DL model also predicted the implant sizes for the same cases.

The exact accuracies of the surgeon's template were 61.54% and 68.38% for predicting femoral and tibial implant sizes, respectively. Meanwhile, the proposed DL model reported exact accuracies of 89.32% and 90.60% for femoral and tibial implant sizes, respectively. The accuracy ± 1 levels of the surgeon and proposed DL model were 97.44% and 97.86%, respectively, for the femoral implant size and 98.72% for both the surgeon and proposed DL model for the tibial implant size.

The observed differences and higher agreement levels achieved by the proposed DL model demonstrate its potential as a valuable tool in preoperative decision-making for TKA. By providing accurate predictions of implant size, the proposed DL model has the potential to optimize implant selection, leading to improved surgical outcomes.

Total knee arthroplasty (TKA) is an end-stage treatment for knee osteoarthritis that relieves pain and loss of mobility, but patient satisfaction and revision rates require improvement. One cause for TKA revision is joint instability, which may be due to improper ligament balancing. A better understanding of the relationship between prosthesis design, alignment, and ligament engagement is necessary to improve component designs and surgical techniques to achieve better outcomes. We investigated the biomechanical effects of ligament model complexity and ligament wrapping during laxity tests using a virtual joint motion simulator. There was little difference in kinematics due to ligament complexity or ligament wrapping.

In recent years, preoperative planning has undergone significant advancements, with a dual focus: improving the accuracy of implant placement and enhancing the prediction of functional outcomes. These breakthroughs have been made possible through the development of advanced processing methods for 3D preoperative images. These methods not only offer novel visualization techniques but can also be seamlessly integrated into computer-aided design models. Additionally, the refinement of motion capture systems has played a pivotal role in this progress. These "markerless" systems are more straightforward to implement and facilitate easier data analysis. Simultaneously, the emergence of machine learning algorithms, utilizing artificial intelligence, has enabled the amalgamation of anatomical and functional data, leading to highly personalized preoperative plans for patients. The shift in preoperative planning from 2D towards 3D, from static to dynamic, is closely linked to technological advances, which will be described in this instructional review. Finally, the concept of 4D planning, encompassing periarticular soft tissues, will be introduced as a forward-looking development in the field of orthopedic surgery.

This case report describes the successful total knee arthroplasty (TKA) in a 58-year-old female with a prior history of multiple knee surgeries. The patient had three prior surgical procedures. The first surgery of the patient was a partial knee replacement, the second surgery the patient underwent was an arthroscopic meniscectomy, and the third surgery was a high tibial osteotomy (HTO) that left her with an extensive amount of scar tissue and a change in physical structure. When scar tissue develops over or close to a joint, the surrounding tissues are pulled inward by this shrinking or contraction. A joint may experience restricted movement as a result of this tightness. Stretchy and excessively flexible joints are common in people with Ehlers-Danlos syndrome. This may become an issue if you need sutures for a wound because the skin is frequently not strong enough to support them. The patient already undergone three surgeries prior but still showed signs of severe pain, swelling, and stiffness in the knee which made the patient suffer more during rest position and also made it sometimes so difficult that it affected everyday tasks. In this situation when the patient consulted the doctors, the patient was suggested to undergo TKA. TKA is the method of orthopedic surgical technique that is most consistently successful and highly effective. Patients with end-stage degenerative knee osteoarthritis might expect reliable results from this surgery. The case demonstrates the preoperative planning, surgical methods, and postoperative care needed to successfully treat a complicated patient profile. Hospital protocols were followed, and the patient's surgery was done with proper care and hygiene.

Despite the increasing number of tools available to predict the outcomes of total knee arthroplasty (TKA), the effect of these predictive tools on patient decision-making remains uncertain.

To assess the effect of an online predictive tool on patient-reported willingness to undergo TKA.

This parallel, double-masked, 2-arm randomized clinical trial compared predictive tool use with treatment as usual (TAU). The study was conducted between June 30, 2022, and July 31, 2023. Participants were followed up for 6 months after enrollment. Participants were recruited from a major Australian private health insurance company and from the surgical waiting list for publicly funded TKA at a tertiary hospital. Eligible participants had unilateral knee osteoarthritis, were contemplating TKA, and had previously tried nonsurgical interventions, such as lifestyle modifications, physiotherapy, and pain medications.

The intervention group was provided access to an online predictive tool at the beginning of the study. This tool offered information regarding the likelihood of improvement in quality of life if patients chose to undergo TKA. The predictions were based on the patient's age, sex, and baseline symptoms. Conversely, the control group received TAU without access to the predictive tool.

The primary outcome measure was the reduction in participants' willingness to undergo surgery at 6 months after tool use as measured by binomial logistic regression. Secondary outcome measures included participant treatment preference and the quality of their decision-making process as measured by the Knee Decision Quality Instrument.

Of 211 randomized participants (mean [SD] age, 65.8 [8.3] years; 118 female [55.9%]), 105 were allocated to the predictive tool group and 106 to the TAU group. After adjusting for baseline differences in willingness for surgery, the predictive tool did not significantly reduce the primary outcome of willingness for surgery at 6 months (adjusted odds ratio, 0.85; 95% CI, 0.42-1.71; P = .64).

Despite the absence of treatment effect on willingness for TKA, predictive tools might still enhance health outcomes of patients with knee osteoarthritis. Additional research is needed to optimize the design and implementation of predictive tools, address limitations, and fully understand their effect on the decision-making process in TKA.

ANZCTR.org.au Identifier: ACTRN12622000072718.

Artificial intelligence (AI) is slowly making its way into all domains and medicine is no exception. AI is already proving to be a promising tool in the health-care field. With respect to orthopedics, AI is already under use in diagnostics as in fracture and tumor detection, predictive algorithms to predict the mortality risk and duration of hospital stay or complications such as implant loosening and in real-time assessment of post-operative rehabilitation. AI could also be of use in surgical training, utilizing technologies such as virtual reality and augmented reality. However, clinicians should also be aware of the limitations of AI as validation is necessary to avoid errors. This article aims to provide a description of AI and its subfields, its current applications in orthopedics, the limitations, and its future prospects.

Artificial intelligence (AI) is increasingly being utilized in orthopedics practice. Ethical concerns have arisen alongside marked improvements and widespread utilization of AI. Patient privacy, consent, data protection, cybersecurity, data safety and monitoring, bias, and accountability are some of the ethical concerns.

The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as 'big data', AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI's limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction.

Artificial intelligence (AI) has become involved in many aspects of everyday life, from voice-activated virtual assistants built into smartphones to global online search engines. Similarly, many areas of modern medicine have found ways to incorporate such technologies into mainstream practice. Despite the enthusiasm, robust evidence to support the utility of AI in contemporary total knee arthroplasty (TKA) remains limited. The purpose of this review was to provide an up-to-date summary of the use of AI in TKA and to explore its current and future value.

Initially, a structured systematic review of the literature was carried out, following PRISMA search principles, with the aim of summarising the understanding of the field and identifying clinical and knowledge gaps.

A limited body of published work exists in this area. Much of the available literature is of poor methodological quality and many published studies could be best described as "demonstration of concepts" rather than "proof of concepts". There exists almost no independent validation of reported findings away from designer/host sites, and the extrapolation of key results to general orthopaedic sites is limited.

While AI has certainly shown value in a small number of specific TKA-associated applications, the majority to date have focused on risk, cost and outcome prediction, rather than surgical care, per se. Extensive future work is needed to demonstrate external validity and reliability in non-designer settings. Well-performed studies are warranted to ensure that the scientific evidence base supporting the use of AI in knee arthroplasty matches the global hype.

Machine learning is a promising and powerful technology with increasing use in orthopedics. Periprosthetic joint infection following total knee arthroplasty results in increased morbidity and mortality. This systematic review investigated the use of machine learning in preventing periprosthetic joint infection.

A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed was searched in November 2022. All studies that investigated the clinical applications of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty were included. Non-English studies, studies with no full text available, studies focusing on non-clinical applications of machine learning, reviews and meta-analyses were excluded. For each included study, its characteristics, machine learning applications, algorithms, statistical performances, strengths and limitations were summarized. Limitations of the current machine learning applications and the studies, including their 'black box' nature, overfitting, the requirement of a large dataset, the lack of external validation, and their retrospective nature were identified.

Eleven studies were included in the final analysis. Machine learning applications in the prevention of periprosthetic joint infection were divided into four categories: prediction, diagnosis, antibiotic application and prognosis.

Machine learning may be a favorable alternative to manual methods in the prevention of periprosthetic joint infection following total knee arthroplasty. It aids in preoperative health optimization, preoperative surgical planning, the early diagnosis of infection, the early application of suitable antibiotics, and the prediction of clinical outcomes. Future research is warranted to resolve the current limitations and bring machine learning into clinical settings.

Computer technologies play a crucial role in orthopaedic surgery and are essential in personalising different treatments. Recent advances allow the usage of augmented reality (AR) for many orthopaedic procedures, which include different types of knee surgery. AR assigns the interaction between virtual environments and the physical world, allowing both to intermingle (AR superimposes information on real objects in real-time) through an optical device and allows personalising different processes for each patient. This article aims to describe the integration of fiducial markers in planning knee surgeries and to perform a narrative description of the latest publications on AR applications in knee surgery. Augmented reality-assisted knee surgery is an emerging set of techniques that can increase accuracy, efficiency, and safety and decrease the radiation exposure (in some surgical procedures, such as osteotomies) of other conventional methods. Initial clinical experience with AR projection based on ArUco-type artificial marker sensors has shown promising results and received positive operator feedback. Once initial clinical safety and efficacy have been demonstrated, the continued experience should be studied to validate this technology and generate further innovation in this rapidly evolving field.

There is a growing demand for total joint arthroplasty (TJA) surgery. The applications of machine learning (ML), mathematical optimization, and computer simulation have the potential to improve efficiency of TJA care delivery through outcome prediction and surgical scheduling optimization, easing the burden on health-care systems. The purpose of this study was to evaluate strategies using advances in analytics and computational modeling that may improve planning and the overall efficiency of TJA care.

A systematic review including MEDLINE, Embase, and IEEE Xplore databases was completed from inception to October 3, 2022, for identification of studies generating ML models for TJA length of stay, duration of surgery, and hospital readmission prediction. A scoping review of optimization strategies in elective surgical scheduling was also conducted.

Twenty studies were included for evaluating ML predictions and 17 in the scoping review of scheduling optimization. Among studies generating linear or logistic control models alongside ML models, only 1 found a control model to outperform its ML counterpart. Furthermore, neural networks performed superior to or at the same level as conventional ML models in all but 1 study. Implementation of mathematical and simulation strategies improved the optimization efficiency when compared to traditional scheduling methods at the operational level.

High-performing predictive ML-based models have been developed for TJA, as have mathematical strategies for elective surgical scheduling optimization. By leveraging artificial intelligence for outcome prediction and surgical optimization, there exist greater opportunities for improved resource utilization and cost-savings in TJA than when using traditional modeling and scheduling methods.

Polyp segmentation is an important task in early identification of colon polyps for prevention of colorectal cancer. Numerous methods of machine learning have been utilized in an attempt to solve this task with varying levels of success. A successful polyp segmentation method which is both accurate and fast could make a huge impact on colonoscopy exams, aiding in real-time detection, as well as enabling faster and cheaper offline analysis. Thus, recent studies have worked to produce networks that are more accurate and faster than the previous generation of networks (e.g., NanoNet). Here, we propose ResPVT architecture for polyp segmentation. This platform uses transformers as a backbone and far surpasses all previous networks not only in accuracy but also with a much higher frame rate which may drastically reduce costs in both real time and offline analysis and enable the widespread application of this technology.