Artificial intelligence in orthopaedic surgery

Simulation-based learning in orthopaedics: q s r

Introduction

Simulation-based learning has emerged as a transformative tool in orthopaedic education, significantly improving surgical training and patient safety. This systematic review examines the role of simulation in enhancing technical skills, decision-making, and clinical competence among orthopaedic trainees.

Methods

A systematic review was conducted to assess the effectiveness of simulation-based training in orthopaedics. Various simulation modalities, including virtual reality (VR), augmented reality (AR), haptic feedback systems, and task-based trainers, were analyzed for their impact on skill acquisition and retention. The study was registered with PROSPERO (ID: CRD420250652679).

Results

Key findings suggest that simulation-based training leads to reduced surgical errors, faster learning curves, and better skill retention. However, challenges such as high costs, limited access to advanced simulation tools, and difficulties in integrating these technologies into traditional curricula persist.

Conclusion

Simulation is expected to play a crucial role in modernizing orthopaedic education by providing safe, repeatable practice opportunities. Future directions include AI-driven training modules and collaborative VR platforms to further enhance training efficacy and patient outcomes.

Readability of Orthopaedic Patient Educational Material: An artificial intelligence application

Background

This study aims to determine the efficacy of the use of artificial intelligence (AI) in rewriting orthopaedic trauma hospital patient educational materials to a patient-appropriate reading level.

Materials and methods

35 orthopaedic patient educational articles were identified from three hospital networks with Level 1 Trauma Centers, categorized based on average reading level. They were run through a formatting Python code, and then a secondary code to determine readability metrics outlined in Table 1. The articles were then rewritten via four iterations of Generative Pre-Trained Transformer (GPT) AI language models. Each model was given the same prompt, outlined in Fig. 1, to rewrite the articles to a 6th grade reading level per AMA recommendations. The rewritten articles were checked for accuracy and formatted and scored to determine mean reading level. Additional analysis was run comparing 9 different AI models from 3 different companies, using the same prompt, comparing cost and percent token reduction.

Results

All GPT AI models lowered the mean combined grade level (Table 2). Fig. 2 compares each GPT model's output to the original articles reading grade level. The oldest model (GPT-3.5-Turbo) was the least consistent and least effective. GPT-4o-Mini and GPT-4o were the most effective and consistent regardless of original article difficulty. Table 3 outlines the cost of running all 35 articles through each GPT model. The most accurate model (GPT-4o) was only $0.61; however, there was only a 0.421 % increase in effectiveness comparing GPT-4o vs. GPT-4o-Mini, at a 175.38 % increase in cost. All GPT rewritten articles were screened for accuracy and determined to have no falsified information or medical inaccuracies. Expanded analysis across 9 AI models is demonstrated in Fig. 4. Fig. 5 compares cost and percent token reduction.

Conclusion

AI is a viable option for reducing the reading difficulty of patient educational materials while maintaining accuracy. Of the models included for analysis, GPT-4o-Mini appears to be the most efficient language model when considering effectiveness, cost, and maintenance of the information included in the original articles.

ChatGPT versus expert arthroplasty surgeons in total knee arthroplasty patient counseling

BACKGROUND

This study aimed to assess the effectiveness of AI compared directly with expert arthroplasty surgeons regarding patient counseling for total knee arthroplasty (TKA).

METHODS

A set of 10 commonly asked generic and nonspecific, single-step patient questions were selected based on review of existing patient resources and expert consensus. Responses were then collected from ChatGPT-4.0 as well as five expert arthroplasty attendings at our institution. A, B, C, D, and E represent attending responses, while F represents the ChatGPT responses. The collected responses were then blinded and independently assessed by the same five arthroplasty surgeons using a five-point Likert scale in four performance areas including empathy, accuracy, completeness, and overall quality. Average scores for each question were determined.

RESULTS

Set F, the ChatGPT answers scored significantly higher than sets A, B, and D in all categories. However, set F did not differ significantly from set C, and E in all the categories. The mean score for set D was above a mean of 4, above neutral, for all four categories. This was only the case for sets C and E.When the attendings scores were combined and compared with ChatGPT, the latter had higher ratings for empathy (4.4 vs. 3.5), accuracy (4.4 vs. 3.7), completeness (4.4 vs. 3.5), and overall quality (4.4 vs. 3.6) (P < 0.001).

CONCLUSION

A preliminary evaluation of ChatGPT-4.0 shows potential for large language AI models to serve as a supplementary resource of patients considering TKA.

Editorial Commentary: Experts in Shoulder Surgery Do Not Consistently Detect Artificial Intelligence-Generated Scientific Abstracts

There has been exponential growth in the number of artificial intelligence (AI)- and machine learning (ML)-related publications in recent years. For example, in the field of shoulder and elbow surgery, there was a 6-fold increase in the number of publications between 2018 and 2021. AI shows the potential to improve diagnostic precision, generate precise surgical templates, direct personalized treatment plans, and reduce administrative costs. However, although AI and ML technology has the ability to positively impact biomedical research, it should be closely monitored and used with extreme caution in the realm of research and scientific writing. Current large language models raise concerns regarding the veracity of AI-generated content, copyright and ownership infringement, fabricated references, lack of in-text citations, plagiarism, and questions of authorship. Recent research has shown that even the most experienced surgeons are unable to consistently detect AI-generated scientific writing. Of note, AI detection software is more adept in this role. AI should be used with caution in the development and production of scholarly work.

Artificial intelligence demonstrates potential to enhance orthopaedic imaging across multiple modalities: A systematic review

Purpose

While several artificial intelligence (AI)-assisted medical imaging applications are reported in the recent orthopaedic literature, comparison of the clinical efficacy and utility of these applications is currently lacking. The aim of this systematic review is to evaluate the effectiveness and reliability of AI applications in orthopaedic imaging, focusing on their impact on diagnostic accuracy, image segmentation and operational efficiency across various imaging modalities.

Methods

Based on the PRISMA guidelines, a comprehensive literature search of PubMed, Cochrane and Scopus databases was performed, using combinations of keywords and MeSH descriptors ('AI', 'ML', 'deep learning', 'orthopaedic surgery' and 'imaging') from inception to March 2024. Included were studies published between September 2018 and February 2024, which evaluated machine learning (ML) model effectiveness in improving orthopaedic imaging. Studies with insufficient data regarding the output variable used to assess the reliability of the ML model, those applying deterministic algorithms, unrelated topics, protocol studies, and other systematic reviews were excluded from the final synthesis. The Joanna Briggs Institute (JBI) Critical Appraisal tool and the Risk Of Bias In Non-randomised Studies-of Interventions (ROBINS-I) tool were applied for the assessment of bias among the included studies.

Results

The 53 included studies reported the use of 11.990.643 images from several diagnostic instruments. A total of 39 studies reported details in terms of the Dice Similarity Coefficient (DSC), while both accuracy and sensitivity were documented across 15 studies. Precision was reported by 14, specificity by nine, and the F1 score by four of the included studies. Three studies applied the area under the curve (AUC) method to evaluate ML model performance. Among the studies included in the final synthesis, Convolutional Neural Networks (CNN) emerged as the most frequently applied category of ML models, present in 17 studies (32%).

Conclusion

The systematic review highlights the diverse application of AI in orthopaedic imaging, demonstrating the capability of various machine learning models in accurately segmenting and analysing orthopaedic images. The results indicate that AI models achieve high performance metrics across different imaging modalities. However, the current body of literature lacks comprehensive statistical analysis and randomized controlled trials, underscoring the need for further research to validate these findings in clinical settings.

Level of evidence

Systematic Review; Level of evidence IV.

External validation of a machine learning prediction model for massive blood loss during surgery for spinal metastases: a multi-institutional study using 880 patients

BACKGROUND CONTEXT

A machine learning (ML) model was recently developed to predict massive intraoperative blood loss (>2,500 mL) during posterior decompressive surgery for spinal metastasis that performed well on external validation within the same region in China.

PURPOSE

We sought to externally validate this model across new geographic regions (North America and Europe) and patient cohorts.

STUDY DESIGN

Multi-institutional retrospective cohort study PATIENT SAMPLE: We retrospectively included patients 18 years or older who underwent decompressive surgery for spinal metastasis across three institutions in the United States, the United Kingdom and the Netherlands between 2016 and 2022. Inclusion and exclusion criteria were consistent with the development study with additional inclusion of (1) patients undergoing palliative decompression without stabilization, (2) patients with multiple myeloma and lymphoma, and (3) patients who continued anticoagulants perioperatively.

OUTCOME MEASURES

Model performance was assessed by comparing the incidence of massive intraoperative blood loss (>2,500 mL) in our cohort to the predicted risk generated by the ML model. Blood loss was quantified in 7 ways (including the formula from the development study) as no gold standard exists, and the method in the development paper was not clearly defined. We estimated blood loss using the anesthesia report, and calculated it using transfusion data, and preoperative and postoperative hematocrit levels.

METHODS

The following five input variables necessary for risk calculation by the ML model were manually collected: tumor type, smoking status, ECOG score, surgical process, and preoperative platelet count. Model performance was assessed on overall fit (Brier score), discriminatory ability (area under the curve (AUC)), calibration (intercept & slope), and clinical utility (decision curve analysis)) for the total validation cohort, and for the North American and European cohorts separately. A sub-analysis, excluding the additional included patient groups, assessed the predictive model's performance with the same inclusion and exclusion criteria as the development cohort.

RESULTS

A total of 880 patients were included with a massive blood loss incidence ranging from 5.3% to 18% depending on the quantification method used. Using the most favorable quantification method, the predictive model overestimated risk in our total validation cohort and scored poorly on overall fit (Brier score: 0.278), discrimination (AUC: 0.631 [95%CI: 0.583, 0.680]), calibration, (intercept: -2.082, [95%CI: -2.285, -1.879]), slope: 0.283 [95%CI: 0.173, 0.393]), and clinical utility, with net harm observed in decision curve analysis from 20%. Similar poor performance results were observed in the sub-analysis excluding the additional included patients (n=676) and when analyzing the North American (n=539) and European (n=341) cohorts separately.

CONCLUSIONS

To our knowledge, this is the first published external validation of a predictive ML model within orthopedic surgery to demonstrate poor performance. This poor performance might be attributed to overfitting and sampling bias as the development cohort had an insufficient sample size, and distributional shift as our cohort had key differences in predictive variables used by the model. These findings emphasize the importance of extensive validation in different geographical areas and addressing biases and pitfalls of ML model development before clinical implementation, as untested models may do more harm than good.

Optimizing the clinical adoption of total joint replacement of the lumbar spine through imaging, robotics and artificial intelligence

INTRODUCTION

The objective of this article is to assess the potential of imaging, robotics, and artificial intelligence (AI) to significantly improve spine care, preoperative planning and surgery.

AREAS COVERED

This article describes the development of lumbar total joint replacement (TJR) of the spine (MOTUS, 3Spine, Chattanooga, TN, U.S.A.). We discuss the evolution of intra-operative imaging, robotics, and AI and how these trends can intersect with lumbar TJR to optimize the safety, efficiency, and accessibility of the procedure.

EXPERT OPINION

By preserving natural spinal motion, TJR represents a significant leap forward in the treatment of degenerative spinal conditions by providing an alternative to fusion. This transformation has already occurred and is continuing to evolve in the primary synovial joints such as hip, knee, shoulder and ankle where arthroplasty outcomes are now so superior that fusion is considered a salvage procedure. The convergence of imaging, robotics and AI is poised to reshape spine care by enhancing precision and safety, personalizing treatment pathways, lowering production costs, and accelerating adoption. However, the key challenges include ensuring continued collaboration between surgeons, researchers, manufacturers, and regulatory bodies to optimize the potential of TJR.

Determining the risk factors for postoperative mechanical complication in degenerative scoliosis: a machine learning approach based on musculoskeletal metrics

OBJECTIVE

To determine the risk factors for mechanical complications (MC) following corrective surgery for degenerative scoliosis through a machine learning (ML) algorithm.

METHODS

Patients with degenerative scoliosis who received corrective surgery were enrolled. A total of 213 cases were ultimately included and randomized into the training set (70%) and test set (30%) to develop the machine learning-based algorithm. The demographic data, comorbidities, regional and global radiographic parameters, paraspinal muscle (PSM) fat infiltration rate (FI%), and vertebral bone quality (VBQ) score were analyzed.

RESULTS

A total of 101 patients (47.4%) had MC, including 46 patients with proximal junctional kyphosis or failure (PJK/PJF), 7 patients with distal junctional kyphosis or failure (DJK/DJF), and 25 patients with rod or screw breakage. In the testing set, Gaussian Naive Bayes (GNB) exhibited the highest AUC at 0.77, while Random Forest (RF) exhibited the highest PRC at 0.63. GNB, RF, and Logistic Regression (LR) models all achieved an accuracy of 0.69, while RF exhibited the highest sensitivity at 0.60 and lowest Brier score of 0.20. Shapley Additive Explanation (SHAP) analysis identified higher FI% of PSM, elevated VBQ score, higher preoperative T1-pelvic angle (T1PA), and postoperative lordosis maldistribution as major risk factors for MC. Based on RF model, local interpretable model-agnostic explanations (LIME) visualization was successfully developed for individual risk calculation.

CONCLUSION

The RF and GNB models showed the best overall performance. Both RF and GNB models identified top-ranked/major risk factors including higher paraspinal muscle fat infiltration, elevated VBQ score, higher preoperative T1PA angle, and postoperative lordosis maldistribution providing valuable insights for surgical decision-making.

Comparison of Machine Learning Algorithms and Hybrid Computational Intelligence Algorithms for Rehabilitation Classification and Prognosis in Reverse Total Shoulder Arthroplasty

Despite the increasing application of machine learning and computational intelligence algorithms in medicine and physiotherapy, accurate classification and prognosis algorithms for postoperative patients in the rehabilitation phase are still lacking. The present study was carried out in two phases. In Phase I, classification performance of simple machine learning algorithms applied on data of patients suffering of reverse total shoulder arthroplasty (RTSA), examining algorithms' classification accuracy and patients' rehabilitation prognosis. In Phase II, hybrid computational intelligence algorithms were developed and applied in order to search for the minimum possible training set that achieves the maximum classification and prognostic performance. The data included features like age and gender, passive range of available motion of all movements (preoperative and postoperative), visual analog pain scale (preoperative and postoperative), and total rehabilitation time. In Phase I, K-nearest neighbors (ΚΝΝ) classification algorithm and K-means clustering algorithm (GAKmeans) were applied. Also, a genetic algorithm (GA)-based clustering algorithm (GAClust) was also applied. To achieve 100% performance on the test set, KNN used 80% of the data in the training set, whereas K-means and GAClust used 90% and 53.3%, respectively. In Phase II, additional computational intelligence algorithms were developed, namely, GAKNN (Genetic Algorithm K-nearest neighbors), GAKmeans, and GA2Clust (genetic algorithm-based clustering algorithm 2), for genetic algorithm optimization of the training set. Genetic algorithm optimization of the training set using hybrid algorithms in Phase II resulted in 100% performance on the test set by using only 35% of the available data for training. The proposed hybrid algorithms can reliably be used for patients' rehabilitation prognosis.

ChatGPT and Gemini Are Not Consistently Concordant With the 2020 American Academy of Orthopaedic Surgeons Clinical Practice Guidelines When Evaluating Rotator Cuff Injury

PURPOSE

To evaluate the accuracy of suggestions given by ChatGPT and Gemini (previously known as "Bard"), 2 widely used publicly available large language models, to evaluate the management of rotator cuff injuries.

METHODS

The 2020 American Academy of Orthopaedic Surgeons (AAOS) Clinical Practice Guidelines (CPGs) were the basis for determining recommended and non-recommended treatments in this study. ChatGPT and Gemini were queried on 16 treatments based on these guidelines examining rotator cuff interventions. The responses were categorized as "concordant" or "discordant" with the AAOS CPGs. The Cohen κ coefficient was calculated to assess inter-rater reliability.

RESULTS

ChatGPT and Gemini showed concordance with the AAOS CPGs for 13 of the 16 treatments queried (81%) and 12 of the 16 treatments queried (75%), respectively. ChatGPT provided discordant responses with the AAOS CPGs for 3 treatments (19%), whereas Gemini provided discordant responses for 4 treatments (25%). Assessment of inter-rater reliability showed a Cohen κ coefficient of 0.98, signifying agreement between the raters in classifying the responses of ChatGPT and Gemini to the AAOS CPGs as being concordant or discordant.

CONCLUSIONS

ChatGPT and Gemini do not consistently provide responses that align with the AAOS CPGs.

CLINICAL RELEVANCE

This study provides evidence that cautions patients not to rely solely on artificial intelligence for recommendations about rotator cuff injuries.

An AI-Driven Framework for Detecting Bone Fractures in Orthopedic Therapy

This study presents an advanced artificial intelligence-driven framework designed to enhance the speed and accuracy of bone fracture detection, addressing key limitations in traditional diagnostic approaches that rely on manual image analysis. The proposed framework integrates the YOLOv8 object detection model with a ResNet backbone to combine robust feature extraction and precise fracture classification. This combination effectively identifies and categorizes bone fractures within X-ray images, supporting reliable diagnostic outcomes. Evaluated on an extensive data set, the model demonstrated a mean average precision of 0.9 and overall classification accuracy of 90.5%, indicating substantial improvements over conventional methods. These results underscore a potential framework to provide healthcare professionals with a powerful, automated tool for orthopedic diagnostics, enhancing diagnostic efficiency and accuracy in routine and emergency care settings. The study contributes to the field by offering an effective solution for automated fracture detection that aims to improve patient outcomes through timely and accurate intervention.

[Translated article] Exploring the potential of artificial intelligence in traumatology: Conversational answers to specific questions

BACKGROUND AND OBJECTIVE

Generative artificial intelligence is a technology that provides greater connectivity with people through conversational bots ("chatbots"). These bots can engage in dialogue using natural language indistinguishable from humans and are a potential source of information for patients. The aim of this study is to examine the performance of these bots in solving specific issues related to orthopedic surgery and traumatology using questions from the Spanish MIR exam between 2008 and 2023.

MATERIAL AND METHODS

Three "chatbot" models (ChatGPT, Bard and Perplexity) were analyzed by answering 114 questions from the MIR. Their accuracy was compared, the readability of their responses was evaluated, and their dependence on logical reasoning and internal and external information was examined. The type of error was also evaluated in the failures.

RESULTS

ChatGPT obtained 72.81% correct answers, followed by Perplexity (67.54%) and Bard (60.53%). Bard provides the most readable and comprehensive responses. The responses demonstrated logical reasoning and the use of internal information from the question prompts. In 16 questions (14%), all three applications failed simultaneously. Errors were identified, including logical and information failures.

CONCLUSIONS

While conversational bots can be useful in resolving medical questions, caution is advised due to the possibility of errors. Currently, they should be considered as a developing tool, and human opinion should prevail over generative artificial intelligence.

Machine learning is better than surgeons at assessing unicompartmental knee replacement radiographs

BACKGROUND

Poor results occasionally occur after unicompartmental knee replacement (UKR). It is often difficult, even for experienced surgeons, to determine why patients have poor outcomes from radiographs. The aim was to compare the ability of experienced surgeons and machine learning to predict whether patients had poor or excellent outcomes from radiographs.

METHODS

924 one-year anterior-posterior radiographs post-UKR were used to train a machine learning model (ResNet50v2) with a transfer learning approach based on their one-year Oxford Knee Score categories. Two experienced surgeons and the model assessed and categorised 70 radiographs (14 Poor scores; 56 Excellent scores) not used for training according to their expected outcome.

RESULTS

The ResNet50v2 model correctly identified 71% (n = 10) of the patients with a poor score and 46 (82%) of those with an excellent score. In contrast, one surgeon could not identify patients with Poor scores (0%) and the other identified one (7%). Both misidentified 3 of those with Excellent scores. The model visualisation method suggested that estimated classifications were made from image features around the implants.

CONCLUSION

The results suggest that there are radiographical features that relate to poor outcomes, which the surgeons are unaware of. Those the model did not identify may have an extra-articular cause for their poor outcome. Further analysis to identify the features associated with poor outcomes could potentially suggest ways that indications or techniques could be improved so as to decrease the incidence of poor results.

Exploring the potential of Artificial Intelligence in Traumatology: Conversational answers to specific questions

INTRODUCTION

Generative Artificial Intelligence is a technology that provides greater connectivity with people through conversational bots («chatbots»). These bots can engage in dialogue using natural language indistinguishable from humans and are a potential source of information for patients.The aim of this study is to examine the performance of these bots in solving specific issues related to orthopedic surgery and traumatology using questions from the Spanish MIR exam between 2008 and 2023.

MATERIAL AND METHODS

Three «chatbot» models (ChatGPT, Bard and Perplexity) were analyzed by answering 114 questions from the MIR. Their accuracy was compared, the readability of their responses was evaluated, and their dependence on logical reasoning and internal and external information was examined. The type of error was also evaluated in the failures.

RESULTS

ChatGPT obtained 72.81% correct answers, followed by Perplexity (67.54%) and Bard (60.53%).Bard provides the most readable and comprehensive responses. The responses demonstrated logical reasoning and the use of internal information from the question prompts. In 16 questions (14%), all 3 applications failed simultaneously. Errors were identified, including logical and information failures.

CONCLUSIONS

While conversational bots can be useful in resolving medical questions, caution is advised due to the possibility of errors. Currently, they should be considered as a developing tool, and human opinion should prevail over Generative Artificial Intelligence.

Artificial Intelligence and the State of the Art of Orthopedic Surgery

Artificial Intelligence (AI) is rapidly transforming healthcare, particularly in orthopedics, by enhancing diagnostic accuracy, surgical planning, and personalized treatment. This review explores current applications of AI in orthopedics, focusing on its contributions to diagnostics and surgical procedures. Key methodologies such as artificial neural networks (ANNs), convolutional neural networks (CNNs), support vector machines (SVMs), and ensemble learning have significantly improved diagnostic precision and patient care. For instance, CNN-based models excel in tasks like fracture detection and osteoarthritis grading, achieving high sensitivity and specificity. In surgical contexts, AI enhances procedures through robotic assistance and optimized preoperative planning, aiding in prosthetic sizing and minimizing complications. Additionally, predictive analytics during postoperative care enable tailored rehabilitation programs that improve recovery times. Despite these advancements, challenges such as data standardization and algorithm transparency hinder widespread adoption. Addressing these issues is crucial for maximizing AI's potential in orthopedic practice. This review emphasizes the synergistic relationship between AI and clinical expertise, highlighting opportunities to enhance diagnostics and streamline surgical procedures, ultimately driving patient-centric care.

Exploring artificial intelligence in orthopaedics: A collaborative survey from the ISAKOS Young Professional Task Force

Purpose

Through an analysis of findings from a survey about the use of artificial intelligence (AI) in orthopaedics, the aim of this study was to establish a scholarly foundation for the discourse on AI in orthopaedics and to elucidate key patterns, challenges and potential future trajectories for AI applications within the field.

Methods

The International Society of Arthroscopy, Knee Surgery and Orthopaedic Sports Medicine (ISAKOS) Young Professionals Task Force developed a survey to collect feedback on issues related to the use of AI in the orthopaedic field. The survey included 26 questions. Data obtained from the completed questionnaires were transferred to a spreadsheet and then analyzed.

Results

Two hundred and eleven orthopaedic surgeons completed the survey. The survey encompassed responses from a diverse cohort of orthopaedic professionals, predominantly comprising males (92.9%). There was wide representation across all geographic regions. A notable proportion (52.1%) reported uncertainty or lack of differentiation among AI, machine learning and deep learning (47.9%). Respondents identified imaging-based diagnosis (60.2%) as the primary field of orthopaedics poised to benefit from AI. A considerable proportion (25.1%) reported using AI in their practice, with primary reasons including referencing scientific literature/publications (40.3%). The vast majority expressed interest in leveraging AI technologies (95.3%), demonstrating an inclination towards incorporating AI into orthopaedic practice. Respondents indicated specific areas of interest for further study, including prediction of patient outcomes after surgery (30.8%) and image-based diagnosis of osteoarthritis (28%).

Conclusions

This survey demonstrates that there is currently limited use of AI in orthopaedic practice, mainly due to a lack of knowledge about the subject, a lack of proven evidence of its real utility and high costs. These findings are in accordance with other surveys in the literature. However, there is also a high level of interest in its use in the future, in increased study and further research on the subject, so that it can be of real benefit and make AI an integral part of the orthopaedic surgeon's daily work.

Level of Evidence

Level IV, survey study.

A machine learning prediction model for total shoulder arthroplasty procedure duration: an evaluation of surgeon, patient, and shoulder-specific factors

BACKGROUND

Operating room efficiency is of paramount importance for scheduling, cost efficiency, and to allow for the high operating volume required to address the growing demand for arthroplasty. The purpose of this study was to develop a machine learning predictive model for total shoulder arthroplasty (TSA) procedure duration and to identify factors which are predictive of a prolonged procedure.

METHODS

A retrospective review was undertaken of all TSA between 2013 and 2021 in a large academic institution. Patient, surgeon, anesthetic, and shoulder-specific factors were assessed. The duration of time in the operating room was recorded and compared to the human scheduler and electronic health record predicted procedure duration. Two gradient-boosted decision tree regression models were created with both training and validation datasets. The mean squared logarithmic error was chosen as the loss function. The first model (M1) considered patient, surgeon, and anesthetic factors, while the second model (M2) considered shoulder anatomy and pathology specific factors in addition.

RESULTS

Human schedulers' predicted 64.1% of cases accurately, with 26.7% underpredicted and 9.2% overpredicted. M1 successfully predicted 79.7% of cases, with 6.9% underpredicted and 13.4% overpredicted. M2 successfully predicted 82.5% of cases with 8.8% underpredicted and 8.8% overpredicted. M2 was significantly more accurate in predicting anatomic total shoulder arthroplasty compared to reverse (rTSA) (90.6% vs. 78.1%, P < .001).The feature with the greatest impact on the shoulder-specific model's prediction was the historical median procedure duration; followed by the electronic health record prediction, surgeon prediction, patient age, and a traumatic indication. Factors which were associated with underpredicting procedure duration included younger age, traumatic indication, male sex, greater body mass index, and a B2 glenoid.

CONCLUSION

Machine learning predictive models outperformed traditional scheduling, with a model incorporating general and shoulder-specific data providing the most accurate prediction of TSA procedure duration. Integration of modeling has the potential to optimize theater utilization and improve efficiency.

Artificial intelligence awareness and perceptions among pediatric orthopedic surgeons: A cross-sectional observational study

BACKGROUND

Artificial intelligence (AI) is a branch of computer science that allows machines to analyze large datasets, learn from patterns, and perform tasks that would otherwise require human intelligence and supervision. It is an emerging tool in pediatric orthopedic surgery, with various promising applications. An evaluation of the current awareness and perceptions among pediatric orthopedic surgeons is necessary to facilitate AI utilization and highlight possible areas of concern.

AIM

To assess the awareness and perceptions of AI among pediatric orthopedic surgeons.

METHODS

This cross-sectional observational study was conducted using a structured questionnaire designed using QuestionPro online survey software to collect quantitative and qualitative data. One hundred and twenty-eight pediatric orthopedic surgeons affiliated with two groups: Pediatric Orthopedic Chapter of Saudi Orthopedics Association and Middle East Pediatric Orthopedic Society in Gulf Cooperation Council Countries were surveyed.

RESULTS

The pediatric orthopedic surgeons surveyed had a low level of familiarity with AI, with more than 60% of respondents rating themselves as being slightly familiar or not at all familiar. The most positively rated aspect of AI applications for pediatric orthopedic surgery was their ability to save time and enhance productivity, with 61.97% agreeing or strongly agreeing, and only 4.23% disagreeing or strongly disagreeing. Our participants also placed a high priority on patient privacy and data security, with over 90% rating them as quite important or highly important. Additional bivariate analyses suggested that physicians with a higher awareness of AI also have a more positive perception.

CONCLUSION

Our study highlights a lack of familiarity among pediatric orthopedic surgeons towards AI, and suggests a need for enhanced education and regulatory frameworks to ensure the safe adoption of AI.

WCAY object detection of fractures for X-ray images of multiple sites

The WCAY (weighted channel attention YOLO) model, which is meticulously crafted to identify fracture features across diverse X-ray image sites, is presented herein. This model integrates novel core operators and an innovative attention mechanism to enhance its efficacy. Initially, leveraging the benefits of dynamic snake convolution (DSConv), which is adept at capturing elongated tubular structural features, we introduce the DSC-C2f module to augment the model's fracture detection performance by replacing a portion of C2f. Subsequently, we integrate the newly proposed weighted channel attention (WCA) mechanism into the architecture to bolster feature fusion and improve fracture detection across various sites. Comparative experiments were conducted, to evaluate the performances of several attention mechanisms. These enhancement strategies were validated through experimentation on public X-ray image datasets (FracAtlas and GRAZPEDWRI-DX). Multiple experimental comparisons substantiated the model's efficacy, demonstrating its superior accuracy and real-time detection capabilities. According to the experimental findings, on the FracAtlas dataset, our WCAY model exhibits a notable 8.8% improvement in mean average precision (mAP) over the original model. On the GRAZPEDWRI-DX dataset, the mAP reaches 64.4%, with a detection accuracy of 93.9% for the "fracture" category alone. The proposed model represents a substantial improvement over the original algorithm compared to other state-of-the-art object detection models. The code is publicly available at https://github.com/cccp421/Fracture-Detection-WCAY .

Applications of artificial intelligence for adolescent idiopathic scoliosis: mapping the evidence

PURPOSE

Adolescent idiopathic scoliosis (AIS) is a common spinal deformity with varying progression, complicating treatment decisions. Artificial intelligence (AI) and machine learning (ML) are increasingly prominent in orthopedic care, aiding in diagnosis, risk-stratification, and treatment guidance. This scoping review outlines AI applications in AIS.

METHODS

This study followed PRISMA-ScR guidelines and included articles that reported the development, use, or validation of AI models for treating, diagnosing, or predicting clinical outcomes in AIS.

RESULTS

40 full-text articles were included, with most studies published in the last 5 years (77.5%). Common ML techniques were convolutional neural networks (55%), decision trees and random forests (15%), and artificial neural networks (15%). Most AI applications in AIS were for imaging analysis (25/40; 62.5%), focusing on automatic measurement of Cobb angle, and axial vertebral rotation (13/25; 52%) and curve classification/severity (13/25; 52%). Prediction was the second most common application (15/40; 37.5%), with studies predicting curve progression (9/15; 60%), and Cobb angles (9/15; 60%). Only 15 studies (37.5%) reported clinical implementation guidelines for AI in AIS management. 52.5% of studies reported model accuracy, with an average of 85.4%.

CONCLUSION

This review highlights the applications of AI in AIS care, notably including automatic radiographic analysis, curve type classification, prediction of curve progression, and AIS diagnosis. However, the current lack of clear clinical implementation guidelines, model transparency, and external validation of studied models limits clinician trust and the generalizability and applicability of AI in AIS management.

Contemporary insights into spinopelvic mechanics

Understanding spinopelvic mechanics is important for the success of total hip arthroplasty (THA). Despite significant advancements in appreciating spinopelvic balance, numerous challenges remain. It is crucial to recognize the individual variability and postoperative changes in spinopelvic parameters and their consequential impact on prosthetic component positioning to mitigate the risk of dislocation and enhance postoperative outcomes. This review describes the integration of advanced diagnostic approaches, enhanced technology, implant considerations, and surgical planning, all tailored to the unique anatomy and biomechanics of each patient. It underscores the importance of accurately predicting postoperative spinopelvic mechanics, selecting suitable imaging techniques, establishing a consistent nomenclature for spinopelvic stiffness, and considering implant-specific strategies. Furthermore, it highlights the potential of artificial intelligence to personalize care.

Artificial intelligence in traumatology

Aims

The aim of this study was to create artificial intelligence (AI) software with the purpose of providing a second opinion to physicians to support distal radius fracture (DRF) detection, and to compare the accuracy of fracture detection of physicians with and without software support.

Methods

The dataset consisted of 26,121 anonymized anterior-posterior (AP) and lateral standard view radiographs of the wrist, with and without DRF. The convolutional neural network (CNN) model was trained to detect the presence of a DRF by comparing the radiographs containing a fracture to the inconspicuous ones. A total of 11 physicians (six surgeons in training and five hand surgeons) assessed 200 pairs of randomly selected digital radiographs of the wrist (AP and lateral) for the presence of a DRF. The same images were first evaluated without, and then with, the support of the CNN model, and the diagnostic accuracy of the two methods was compared.

Results

At the time of the study, the CNN model showed an area under the receiver operating curve of 0.97. AI assistance improved the physician's sensitivity (correct fracture detection) from 80% to 87%, and the specificity (correct fracture exclusion) from 91% to 95%. The overall error rate (combined false positive and false negative) was reduced from 14% without AI to 9% with AI.

Conclusion

The use of a CNN model as a second opinion can improve the diagnostic accuracy of DRF detection in the study setting.

Recent technological advancements in Artificial Intelligence for orthopaedic wound management

In orthopaedics, wound care is crucial as surgical site infections carry disease burden due to increased length of stay, decreased quality of life and poorer patient outcomes. Artificial Intelligence (AI) has a vital role in revolutionising wound care in orthopaedics: ranging from wound assessment, early detection of complications, risk stratifying patients, and remote patient monitoring. Incorporating AI in orthopaedics has reduced dependency on manual physician assessment which is time-consuming. This article summarises current literature on how AI is used for wound assessment and management in the orthopaedic community.

Editorial Commentary: Machine Learning and Artificial Intelligence Are Valuable Tools yet Dependent on the Data Input

Machine learning is likely to become one of the most valuable tools for predicting outcomes in patients with patellofemoral instability. Traditional statistical analysis is challenging in this diagnosis as the result of the multitude of risk factors. However, 3 important cautions must be considered. (1) Machine learning is limited by the quality of the data entered. Many of the risk factors for patellofemoral instability rely on classification systems with significant interexaminer variability and patient-reported outcomes used to track changes contain inherent biases, especially with regard to race and gender. Poor data quality will lead to unreliable predictions, or "garbage in equals garbage out." (2) The optimal machine-learning algorithms for addressing specific clinical questions remain uncertain. (3) The question of how much data we really need for accurate analysis is unresolved, which again, is completely dependent on the quality of the data. Machine learning is the future; just beware of what goes into the chicken salad.

A millimetre-scale capacitive biosensing and biophysical stimulation system for emerging bioelectronic bone implants

Bioelectronic bone implants are being widely recognized as a promising technology for highly personalized bone/implant interface sensing and biophysical therapeutic stimulation. Such bioelectronic devices are based on an innovative concept with the ability to be applied to a wide range of implants, including in fixation and prosthetic systems. Recently, biointerface sensing using capacitive patterns was proposed to overcome the limitations of standard imaging technologies and other non-imaging technologies; moreover, electric stimulation using capacitive patterns was proposed to overcome the limitations of non-instrumented implants. We here provide an innovative low-power miniaturized electronic system with ability to provide both therapeutic stimulation and bone/implant interface monitoring using network-architectured capacitive interdigitated patterns. It comprises five modules: sensing, electric stimulation, processing, communication and power management. This technology was validated using in vitro tests: concerning the sensing system, its ability to detect biointerface changes ranging from tiny to severe bone-implant interface changes in target regions was validated; concerning the stimulation system, its ability to significantly enhance bone cells' full differentiation, including matrix maturation and mineralization, was also confirmed. This work provides an impactful contribution and paves the way for the development of the new generation of orthopaedic biodevices.

Appropriateness of Frequently Asked Patient Questions Following Total Hip Arthroplasty From ChatGPT Compared to Arthroplasty-Trained Nurses

BACKGROUND

The use of ChatGPT (Generative Pretrained Transformer), which is a natural language artificial intelligence model, has gained unparalleled attention with the accumulation of over 100 million users within months of launching. As such, we aimed to compare the following: 1) orthopaedic surgeons' evaluation of the appropriateness of the answers to the most frequently asked patient questions after total hip arthroplasty; and 2) patients' evaluation of ChatGPT and arthroplasty-trained nurses responses to answer their postoperative questions.

METHODS

We prospectively created 60 questions to address the most commonly asked patient questions following total hip arthroplasty. We obtained answers from arthroplasty-trained nurses and from the ChatGPT-3.5 version for each of the questions. Surgeons graded each set of responses based on clinical judgment as 1) "appropriate," 2) "inappropriate" if the response contained inappropriate information, or 3) "unreliable" if the responses provided inconsistent content. Each patient was given a randomly selected question from the 60 aforementioned questions, with responses provided by ChatGPT and arthroplasty-trained nurses, using a Research Electronic Data Capture survey hosted at our local hospital.

RESULTS

The 3 fellowship-trained surgeons graded 56 out of 60 (93.3%) responses for the arthroplasty-trained nurses and 57 out of 60 (95.0%) for ChatGPT to be "appropriate." There were 175 out of 252 (69.4%) patients who were more comfortable following the ChatGPT responses and 77 out of 252 (30.6%) who preferred arthroplasty-trained nurses' responses. However, 199 out of 252 patients (79.0%) responded that they were "uncertain" with regard to trusting AI to answer their postoperative questions.

CONCLUSIONS

ChatGPT provided appropriate answers from a physician perspective. Patients were also more comfortable with the ChatGPT responses than those from arthroplasty-trained nurses. Inevitably, its successful implementation is dependent on its ability to provide credible information that is consistent with the goals of the physician and patient alike.

Prospective applications of bioactive materials in orthopedic therapies: A review

The biomedical application of biodegradable polymers for addressing bone-related diseases has garnered considerable attention in recent years. Advances in material technology have expanded the repertoire of materials suitable for orthopedic implants, with nanomaterials playing a pivotal role in replicating crucial surface properties akin to natural tissues. This comprehensive review explores the evaluation of bioactive glass ceramics, shedding light on their properties and applications. The synthesis of composites through composite manufacturing has emerged as a strategy to enhance biocompatibility and biomechanical characteristics. They are addressing challenges associated with conventional implants and nanomaterials, whether in the form of functional nano coatings or nanostructured surfaces, present opportunities to refine implant techniques. Novel developments in orthopedic biomaterials, such as smart biomaterials, porous structures, and 3D implants, offer stimuli-responsive behavior to achieve desired implant shapes and characteristics. Bioactive and biodegradable porous polymer/inorganic composite materials are explored for bone tissue engineering scaffolds, aiming to promote bone formation and regeneration. As a prospective direction, the integration of stem cells into scaffolds hints at the creation of next-generation synthetic/living hybrid biomaterials, displaying high adaptability in biological settings. This review establishes a foundation for nanotechnology-driven biomaterials by elucidating fundamental design factors crucial for orthopedic implant performance and their response to cell differentiation, proliferation, and adhesion.

Is it feasible to develop a supervised learning algorithm incorporating spinopelvic mobility to predict impingement in patients undergoing total hip arthroplasty?

Aims

Precise implant positioning, tailored to individual spinopelvic biomechanics and phenotype, is paramount for stability in total hip arthroplasty (THA). Despite a few studies on instability prediction, there is a notable gap in research utilizing artificial intelligence (AI). The objective of our pilot study was to evaluate the feasibility of developing an AI algorithm tailored to individual spinopelvic mechanics and patient phenotype for predicting impingement.

Methods

This international, multicentre prospective cohort study across two centres encompassed 157 adults undergoing primary robotic arm-assisted THA. Impingement during specific flexion and extension stances was identified using the virtual range of motion (ROM) tool of the robotic software. The primary AI model, the Light Gradient-Boosting Machine (LGBM), used tabular data to predict impingement presence, direction (flexion or extension), and type. A secondary model integrating tabular data with plain anteroposterior pelvis radiographs was evaluated to assess for any potential enhancement in prediction accuracy.

Results

We identified nine predictors from an analysis of baseline spinopelvic characteristics and surgical planning parameters. Using fivefold cross-validation, the LGBM achieved 70.2% impingement prediction accuracy. With impingement data, the LGBM estimated direction with 85% accuracy, while the support vector machine (SVM) determined impingement type with 72.9% accuracy. After integrating imaging data with a multilayer perceptron (tabular) and a convolutional neural network (radiograph), the LGBM's prediction was 68.1%. Both combined and LGBM-only had similar impingement direction prediction rates (around 84.5%).

Conclusion

This study is a pioneering effort in leveraging AI for impingement prediction in THA, utilizing a comprehensive, real-world clinical dataset. Our machine-learning algorithm demonstrated promising accuracy in predicting impingement, its type, and direction. While the addition of imaging data to our deep-learning algorithm did not boost accuracy, the potential for refined annotations, such as landmark markings, offers avenues for future enhancement. Prior to clinical integration, external validation and larger-scale testing of this algorithm are essential.

The Use of Artificial Intelligence for Orthopedic Surgical Backlogs Such as the One Following the COVID-19 Pandemic: A Narrative Review

➤ The COVID-19 pandemic created a persistent surgical backlog in elective orthopedic surgeries. ➤ Artificial intelligence (AI) uses computer algorithms to solve problems and has potential as a powerful tool in health care. ➤ AI can help improve current and future orthopedic backlogs through enhancing surgical schedules, optimizing preoperative planning, and predicting postsurgical outcomes. ➤ AI may help manage existing waitlists and increase efficiency in orthopedic workflows.

Emerging Innovations in Preoperative Planning and Motion Analysis in Orthopedic Surgery

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.

Artifical intelligence in surgery: ethical considerations in the light of social trends in the perception of health and medicine

The use of artificial intelligence (AI) in medicine and surgery is currently predicted to be very promising. However, AI has the potential to change the doctor's role and the doctor-patient relationship. It has the potential to support people's desires for health, along with the potential to nudge or push people to behave in a certain way. To understand these potentials, we must see AI in the light of social developments that have brought about changes in how medicine's role, in a given society, is understood. The trends of 'privatisation of medicine' and 'public-healthisation of the private' are proposed as a contextual backdrop to explain why AI raises ethical concerns different from those previously caused by new medical technologies, and which therefore need to be addressed specifically for AI.

Preoperative prediction for periprosthetic bone loss and individual evaluation of bisphosphonate effect after total hip arthroplasty using artificial intelligence

Aims

This study was designed to develop a model for predicting bone mineral density (BMD) loss of the femur after total hip arthroplasty (THA) using artificial intelligence (AI), and to identify factors that influence the prediction. Additionally, we virtually examined the efficacy of administration of bisphosphonate for cases with severe BMD loss based on the predictive model.

Methods

The study included 538 joints that underwent primary THA. The patients were divided into groups using unsupervised time series clustering for five-year BMD loss of Gruen zone 7 postoperatively, and a machine-learning model to predict the BMD loss was developed. Additionally, the predictor for BMD loss was extracted using SHapley Additive exPlanations (SHAP). The patient-specific efficacy of bisphosphonate, which is the most important categorical predictor for BMD loss, was examined by calculating the change in predictive probability when hypothetically switching between the inclusion and exclusion of bisphosphonate.

Results

Time series clustering allowed us to divide the patients into two groups, and the predictive factors were identified including patient- and operation-related factors. The area under the receiver operating characteristic (ROC) curve (AUC) for the BMD loss prediction averaged 0.734. Virtual administration of bisphosphonate showed on average 14% efficacy in preventing BMD loss of zone 7. Additionally, stem types and preoperative triglyceride (TG), creatinine (Cr), estimated glomerular filtration rate (eGFR), and creatine kinase (CK) showed significant association with the estimated patient-specific efficacy of bisphosphonate.

Conclusion

Periprosthetic BMD loss after THA is predictable based on patient- and operation-related factors, and optimal prescription of bisphosphonate based on the prediction may prevent BMD loss.

Artificial intelligence in diagnosing upper limb musculoskeletal disorders: a systematic review and meta-analysis of diagnostic tests

Purpose

The integration of artificial intelligence (AI) in radiology has revolutionized diagnostics, optimizing precision and decision-making. Specifically in musculoskeletal imaging, AI tools can improve accuracy for upper extremity pathologies. This study aimed to assess the diagnostic performance of AI models in detecting musculoskeletal pathologies of the upper extremity using different imaging modalities.

Methods

A meta-analysis was conducted, involving searches on MEDLINE/PubMed, SCOPUS, Cochrane Library, Lilacs, and SciELO. The quality of the studies was assessed using the QUADAS-2 tool. Diagnostic accuracy measures including sensitivity, specificity, diagnostic odds ratio (DOR), positive and negative likelihood ratios (PLR, NLR), area under the curve (AUC), and summary receiver operating characteristic were pooled using a random-effects model. Heterogeneity and subgroup analyses were also included. All statistical analyses and plots were performed using the R software package.

Results

Thirteen models from ten articles were analyzed. The sensitivity and specificity of the AI models to detect musculoskeletal conditions in the upper extremity were 0.926 (95% CI: 0.900; 0.945) and 0.908 (95% CI: 0.810; 0.958). The PLR, NLR, lnDOR, and the AUC estimates were found to be 19.18 (95% CI: 8.90; 29.34), 0.11 (95% CI: 0.18; 0.46), 4.62 (95% CI: 4.02; 5.22) with a (P < 0.001), and 95%, respectively.

Conclusion

The AI models exhibited strong univariate and bivariate performance in detecting both positive and negative cases within the analyzed dataset of musculoskeletal pathologies in the upper extremity.

Association of Radiographic Soft Tissue Thickness With Revision Total Ankle Arthroplasty Following Primary Total Ankle Arthroplasty: A Minimum of 5-year Follow-up

Background

The incidence of primary total ankle arthroplasty (TAA) is rising, with a corresponding increase in revision surgeries. Despite this, research on risk factors for revision TAA following primary TAA remains limited. Radiographic soft tissue thickness has been explored as a potential predictor for outcomes in hip, knee, and shoulder arthroplasty, but its role in TAA has not been assessed. This study aimed to assess the predictive value of radiographic soft tissue thickness for identifying patients at risk of requiring revision surgery following primary TAA.

Methods

A retrospective study was conducted on 323 patients who underwent primary TAA between 2003 and 2019. Radiographic measurements of soft tissue thickness were obtained from preoperative radiographs. Two novel radiographic measures of soft tissue thickness were developed and assessed (tibial tissue thickness and talus tissue thickness). Clinical variables including age, gender, body mass index (BMI), American Society of Anesthesiologists (ASA) classification, diabetes, smoking status, primary diagnosis, and implant type were recorded. Logistic regression analysis was used to assess the predictive value of soft tissue thickness and BMI for revision TAA.

Results

The rate of revision surgery was 4.3% (14 of 323 patients). Patients requiring revision had significantly greater tibial tissue (3.54 vs 2.48 cm; P = .02) and talus tissue (2.79 vs 2.42 cm; P = .02) thickness compared with those not requiring revision. Both the tibial tissue thickness (odds ratio 1.16 [1.12-1.20]; P < .01) and the talus tissue thickness (odds ratio: 1.10 [1.05-1.15]; P < .01) measurements were significant predictors of revision TAA in multivariable logistic regression models. However, BMI was not a significant predictor of revision TAA. The two metrics demonstrated excellent interrater reliability.

Conclusion

Greater soft tissue thickness was a better predictor of revision TAA compared with BMI. These findings suggest that radiographic soft tissue thickness may be a valuable tool for assessing the risk of the need for revision TAA following primary TAA. Further research is needed to validate and explore the potential impact on clinical practice.

Level of Evidence

Level III, comparative study.

Recommended Requirements and Essential Elements for Proper Reporting of the Use of Artificial Intelligence Machine Learning Tools in Biomedical Research and Scientific Publications

Essential elements required for proper use of artificial intelligence machine learning tools in biomedical research and scientific publications include (1) explanation justifying why a machine learning approach contributes to the purpose of the study; (2) description of the adequacy of the data (input) to produce the desired results (output); (3) details of the algorithmic (i.e., computational) approach including methods for organizing the data (preprocessing); the machine learning computational algorithm(s) assessed; on what data the models were trained; the presence of bias and efforts to mitigate these effects; and the methods for quantifying the variables (features) most influential in determining the results (e.g., Shapley values); (4) description of methods, and reporting of results, quantitating performance in terms of both model accuracy and model calibration (level of confidence in the model's predictions); (5) availability of the programming code (including a link to the code when available-ideally, the code should be available); (6) discussion of model internal validation (results applicable and sensitive to the population investigated and data on which the model was trained) and external validation (were the results investigated as to whether they are generalizable to different populations? If not, consideration of this limitation and discussion of plans for external validation, i.e., next steps). As biomedical research submissions using artificial intelligence technology increase, these requirements could facilitate purposeful use and comprehensive methodological reporting.

Reliability of artificial intelligence in predicting total knee arthroplasty component sizes: a systematic review

PURPOSE

This systematic review aimed to investigate the reliability of AI predictive models of intraoperative implant sizing in total knee arthroplasty (TKA).

METHODS

Four databases were searched from inception till July 2023 for original studies that studied the reliability of AI prediction in TKA. The primary outcome was the accuracy ± 1 size. This review was conducted per PRISMA guidelines, and the risk of bias was assessed using the MINORS criteria.

RESULTS

A total of four observational studies comprised of at least 34,547 patients were included in this review. A mean MINORS score of 11 out of 16 was assigned to the review. All included studies were published between 2021 and 2022, with a total of nine different AI algorithms reported. Among these AI models, the accuracy of TKA femoral component sizing prediction ranged from 88.3 to 99.7% within a deviation of one size, while tibial component sizing exhibited an accuracy ranging from 90 to 99.9% ± 1 size.

CONCLUSION

This study demonstrated the potential of AI as a valuable complement for planning TKA, exhibiting a satisfactory level of reliability in predicting TKA implant sizes. This predictive accuracy is comparable to that of the manual and digital templating techniques currently documented in the literature. However, future research is imperative to assess the impact of AI on patient care and cost-effectiveness.

LEVEL OF EVIDENCE III

PROSPERO registration number: CRD42023446868.

The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives

The remarkable progress in data aggregation and deep learning algorithms has positioned artificial intelligence (AI) and machine learning (ML) to revolutionize the field of medicine. AI is becoming more and more prevalent in the healthcare sector, and its impact on orthopedic surgery is already evident in several fields. This review aims to examine the literature that explores the comprehensive clinical relevance of AI-based tools utilized before, during, and after anterior cruciate ligament (ACL) reconstruction. The review focuses on current clinical applications and future prospects in preoperative management, encompassing risk prediction and diagnostics; intraoperative tools, specifically navigation, identifying complex anatomic landmarks during surgery; and postoperative applications in terms of postoperative care and rehabilitation. Additionally, AI tools in educational and training settings are presented. Orthopedic surgeons are showing a growing interest in AI, as evidenced by the applications discussed in this review, particularly those related to ACL injury. The exponential increase in studies on AI tools applicable to the management of ACL tears promises a significant future impact in its clinical application, with growing attention from orthopedic surgeons.

ChatGPT and large language models in orthopedics: from education and surgery to research

ChatGPT has quickly popularized since its release in November 2022. Currently, large language models (LLMs) and ChatGPT have been applied in various domains of medical science, including in cardiology, nephrology, orthopedics, ophthalmology, gastroenterology, and radiology. Researchers are exploring the potential of LLMs and ChatGPT for clinicians and surgeons in every domain. This study discusses how ChatGPT can help orthopedic clinicians and surgeons perform various medical tasks. LLMs and ChatGPT can help the patient community by providing suggestions and diagnostic guidelines. In this study, the use of LLMs and ChatGPT to enhance and expand the field of orthopedics, including orthopedic education, surgery, and research, is explored. Present LLMs have several shortcomings, which are discussed herein. However, next-generation and future domain-specific LLMs are expected to be more potent and transform patients' quality of life.

Artificial intelligence in orthopaedics

Cite this article: Bone Joint Res  2023;12(8):494–496.

Transforming Orthopedic Joint Surgeries: The Role of Artificial Intelligence (AI) and Robotics

The landscape of orthopedic joint surgeries, specifically total hip arthroplasty (THA) and total knee arthroplasty (TKA), is rapidly changing, and artificial intelligence (AI) along with robotics is at the helm of this transformation. These technologies, working synergistically, have introduced unprecedented levels of precision and personalization to surgical procedures, thereby significantly enhancing patient outcomes. In this editorial, we explore the changing perspectives of orthopedic surgeons toward AI and robotics and dissect the incorporation of these technologies in surgeries, their associated advantages, their inherent limitations, and potential future prospects. We draw from a host of recent studies to provide a comprehensive understanding of how these transformative technologies can augment surgical performance and patient care.