Machine Learning in Orthopedics: A Literature Review

Can we identify abnormal pelvic tilt using pre-THA anteroposterior pelvic radiographs?

BACKGROUND

Patients with increased pelvic tilt (PT) are at risk for instability following total hip arthroplasty (THA). Identification of increased PT using anteroposterior (AP) pelvic radiographs could avoid additional spinopelvic radiographs. This study aimed to (1) describe which AP pelvic parameters most accurately estimate sagittal PT, and (2) determine thresholds for these parameters that can identify patients with increased PT.

METHODS

This was a retrospective, consecutive, cohort study in a tertiary referral hospital on 225 patients (age: 66 ± 12 years-old; 52% female) listed for THA. Patients underwent pre-operative standing AP pelvic radiographs to measure distance- and angular- based parameters from several anatomical landmarks. Sagittal PT was measured on a standing lateral spinopelvic radiograph and considered high when ≥ 20°.

RESULTS

No AP pelvic parameters correlated strongly with sagittal PT. Ratio between horizontal and vertical diameter of the pelvic foramen (C/D ratio) (rho - 0.341; p < 0.001); and vertical distance between trans-SIJ and trans-ASIS line (SITA) (rho 0.307; p < 0.001) correlated moderately with sagittal PT. Sacro-femoral-pubic (SFP) angle < 60° had highest sensitivity (85%), but lowest specificity (52%) to differentiate between patients with and without increased PT. If SITA > 62 mm, C/D ratio < 0.5 and SFP < 60°, specificity increased (88%), but sensitivity was low (49%).

CONCLUSION

In the absence of computerized models, AP pelvic parameters cannot accurately predict sagittal PT. However, an SFP < 60° should alert a hip surgeon that a patient may have an increased PT, and would benefit from additional lateral spinopelvic imaging prior to THA.

LEVEL OF EVIDENCE

Level II, diagnostic study.

Machine learning for predicting duration of surgery and length of stay: A literature review on joint arthroplasty

INTRODUCTION

In recent years, different factors such as population aging have caused escalating demand for hip and knee arthroplasty straining already limited hospitals' resources. To address this challenge, focus is put on medical and operational efficiency improvements. This includes an increased use of machine learning (ML) to predict duration of surgery (DOS) and length of stay (LOS) for total knee and total hip arthroplasty, which can be utilized for optimizing resource allocation to satisfy medical and operational limitations. This paper explores the development and performance of ML models in predicting DOS and LOS.

METHODS

A systematic search of publications between 2010-2023 was conducted following PRISMA guidelines. Considering the inclusion and exclusion criteria, 28 out of 722 gathered papers from PubMed, Web of Science, and manual search were included in the study. Descriptive statistics was used to analyze the extracted data regarding data preprocessing, model development, and model performance assessment.

RESULTS

Most of the papers work on LOS as a binary variable. Patient's age was identified as the most frequently used and reported as important variable for predicting DOS and LOS. Investigations also illustrated that within the resulting 28 papers, more than 71% of models reached good to perfect performance based on the area under the receiver operating characteristic curve (AUC), where artificial neural networks and ensemble learning models had the biggest share among the best-performing models.

CONCLUSION

The utilization of ML models is increasing in the literature. The current performance level indicates that ML can potentially turn to powerful tools in predicting DOS and LOS for different purposes. Meanwhile, the literature is not matured yet in reporting real-life application. Future studies can focus on model specification and validation by considering empirical application.

ChatGPT Can Offer At Least Satisfactory Responses to Common Patient Questions Regarding Hip Arthroscopy

PURPOSE

To assess the accuracy of answers provided by ChatGPT 4.0 (an advanced language model developed by OpenAI) regarding 25 common patient questions about hip arthroscopy.

METHODS

ChatGPT 4.0 was presented with 25 common patient questions regarding hip arthroscopy with no follow-up questions and repetition. Each response was evaluated by 2 board-certified orthopaedic sports medicine surgeons independently. Responses were rated, with scores of 1, 2, 3, and 4 corresponding to "excellent response not requiring clarification," "satisfactory requiring minimal clarification," "satisfactory requiring moderate clarification," and "unsatisfactory requiring substantial clarification," respectively.

RESULTS

Twenty responses were rated "excellent" and 2 responses were rated "satisfactory requiring minimal clarification" by both of reviewers. Responses to questions "What kind of anesthesia is used for hip arthroscopy?" and "What is the average age for hip arthroscopy?" were rated as "satisfactory requiring minimal clarification" by both reviewers. None of the responses were rated as "satisfactory requiring moderate clarification" or "unsatisfactory" by either of the reviewers.

CONCLUSIONS

ChatGPT 4.0 provides at least satisfactory responses to patient questions regarding hip arthroscopy. Under the supervision of an orthopaedic sports medicine surgeon, it could be used as a supplementary tool for patient education.

CLINICAL RELEVANCE

This study compared the answers of ChatGPT to patients' questions regarding hip arthroscopy with the current literature. As ChatGPT has gained popularity among patients, the study aimed to find if the responses that patients get from this chatbot are compatible with the up-to-date literature.

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.

Validating the Accuracy of a Patient-Facing Clinical Decision Support System in Predicting Lumbar Disc Herniation: Diagnostic Accuracy Study

BACKGROUND

Low back pain (LBP) is a major cause of disability globally, and the diagnosis of LBP is challenging for clinicians.

OBJECTIVE

Using new software called Therapha, this study aimed to assess the accuracy level of artificial intelligence as a Clinical Decision Support System (CDSS) compared to MRI in predicting lumbar disc herniated patients.

METHODS

One hundred low back pain patients aged ≥18 years old were included in the study. The study was conducted in three stages. Firstly, a case series was conducted by matching MRI and Therapha diagnosis for 10 patients. Subsequently, Delphi methodology was employed to establish a clinical consensus. Finally, to determine the accuracy of the newly developed software, a cross-sectional study was undertaken involving 100 patients.

RESULTS

The software showed a significant diagnostic accuracy with the area under the curve in the ROC analysis determined as 0.84 with a sensitivity of 88% and a specificity of 80%.

CONCLUSIONS

The study's findings revealed that CDSS using Therapha has a reasonable level of efficacy, and this can be utilized clinically to acquire a faster and more accurate screening of patients with lumbar disc herniation.

Machine-Learning Models Reliably Predict Clinical Outcomes in Medial Patellofemoral Ligament Reconstruction

PURPOSE

To develop a machine-learning model to predict clinical outcomes after medial patellofemoral ligament reconstruction (MPFLR) and identify the important predictive indicators.

METHODS

This study included patients who underwent MPFLR from January 2018 to December 2022. The exclusion criteria were as follows: (1) concurrent bony procedures, (2) history of other knee surgeries, and (3) follow-up period of less than 12 months. Forty-two predictive models were constructed for 7 clinical outcomes (failure to achieve minimum clinically important difference of clinical scores, return to preinjury sports, pivoting sports, and recurrent instability) using 6 machine-learning algorithms (random forest, logistic regression, support vector machine, decision tree, implemented multilayer perceptron, and K-nearest neighbor). The performance of the model was evaluated using metrics such as the area under the receiver operating characteristic curve, accuracy, specificity, and sensitivity. In addition, SHapley Additive exPlanation summary plot was employed to identify the important predictive factors of the best-performing model.

RESULTS

A total of 218 patients met criteria. For the best-performing models in predicting failure to achieve the minimum clinically important difference for Lysholm, International Knee Documentation Committee, Kujala, and Tegner scores, the area under the receiver operating characteristic curves and accuracies were 0.884 (good) and 87.3%, 0.859 (good) and 86.2%, 0.969 (excellent) and 97.0%, and 0.760 (fair) and 76.8%, respectively; 0.952 (excellent) and 95.2% for return to preinjury sports; 0.756 (fair) and 75.4% for return to pivoting sports; and 0.943 (excellent) and 94.9% for recurrent instability. Low preoperative Tegner score, shorter time to surgery, and absence of severe trochlear dysplasia were significant predictors for return to preinjury sports, whereas the absence of severe trochlear dysplasia and patellar alta were significant predictors for return to pivoting sports. Older age, female sex, and low preoperative Lysholm score were highly predictive of recurrent instability.

CONCLUSIONS

The predictive models developed using machine-learning algorithms can reliably forecast the clinical outcomes of MPFLR, particularly demonstrating excellent performance in predicting recurrent instability.

LEVEL OF EVIDENCE

Level III, case-control study.

Attitude of aspiring orthopaedic surgeons towards artificial intelligence: a multinational cross-sectional survey study

INTRODUCTION

The purpose of this study was to evaluate the perspectives of aspiring orthopaedic surgeons on artificial intelligence (AI), analysing how gender, AI knowledge, and technical inclination influence views on AI. Additionally, the extent to which recent AI advancements sway career decisions was assessed.

MATERIALS AND METHODS

A digital survey was distributed to student members of orthopaedic societies across Germany, Switzerland, and Austria. Subgroup analyses explored how gender, AI knowledge, and technical inclination shape attitudes towards AI.

RESULTS

Of 174 total respondents, 86.2% (n = 150) intended to pursue a career in orthopaedic surgery and were included in the analysis. The majority (74.5%) reported 'basic' or 'no' knowledge about AI. Approximately 29.3% believed AI would significantly impact orthopaedics within 5 years, with another 35.3% projecting 5-10 years. AI was predominantly seen as an assistive tool (77.8%), without significant fear of job displacement. The most valued AI applications were identified as preoperative implant planning (85.3%), administrative tasks (84%), and image analysis (81.3%). Concerns arose regarding skill atrophy due to overreliance (69.3%), liability (68%), and diminished patient interaction (56%). The majority maintained a 'neutral' view on AI (53%), though 32.9% were 'enthusiastic'. A stronger focus on AI in medical education was requested by 81.9%. Most participants (72.8%) felt recent AI advancements did not alter their career decisions towards or away from the orthopaedic specialty. Statistical analysis revealed a significant association between AI literacy (p = 0.015) and technical inclination (p = 0.003). AI literacy did not increase significantly during medical education (p = 0.091).

CONCLUSIONS

Future orthopaedic surgeons exhibit a favourable outlook on AI, foreseeing its significant influence in the near future. AI literacy remains relatively low and showed no improvement during medical school. There is notable demand for improved AI-related education. The choice of orthopaedics as a specialty appears to be robust against the sway of recent AI advancements.

LEVEL OF EVIDENCE

Cross-sectional survey study; level IV.

Second opinion machine learning for fast-track pathway assignment in hip and knee replacement surgery: the use of patient-reported outcome measures

BACKGROUND

The frequency of hip and knee arthroplasty surgeries has been rising steadily in recent decades. This trend is attributed to an aging population, leading to increased demands on healthcare systems. Fast Track (FT) surgical protocols, perioperative procedures designed to expedite patient recovery and early mobilization, have demonstrated efficacy in reducing hospital stays, convalescence periods, and associated costs. However, the criteria for selecting patients for FT procedures have not fully capitalized on the available patient data, including patient-reported outcome measures (PROMs).

METHODS

Our study focused on developing machine learning (ML) models to support decision making in assigning patients to FT procedures, utilizing data from patients' self-reported health status. These models are specifically designed to predict the potential health status improvement in patients initially selected for FT. Our approach focused on techniques inspired by the concept of controllable AI. This includes eXplainable AI (XAI), which aims to make the model's recommendations comprehensible to clinicians, and cautious prediction, a method used to alert clinicians about potential control losses, thereby enhancing the models' trustworthiness and reliability.

RESULTS

Our models were trained and tested using a dataset comprising 899 records from individual patients admitted to the FT program at IRCCS Ospedale Galeazzi-Sant'Ambrogio. After training and selecting hyper-parameters, the models were assessed using a separate internal test set. The interpretable models demonstrated performance on par or even better than the most effective 'black-box' model (Random Forest). These models achieved sensitivity, specificity, and positive predictive value (PPV) exceeding 70%, with an area under the curve (AUC) greater than 80%. The cautious prediction models exhibited enhanced performance while maintaining satisfactory coverage (over 50%). Further, when externally validated on a separate cohort from the same hospital-comprising patients from a subsequent time period-the models showed no pragmatically notable decline in performance.

CONCLUSIONS

Our results demonstrate the effectiveness of utilizing PROMs as basis to develop ML models for planning assignments to FT procedures. Notably, the application of controllable AI techniques, particularly those based on XAI and cautious prediction, emerges as a promising approach. These techniques provide reliable and interpretable support, essential for informed decision-making in clinical processes.

Artificial intelligence in total and unicompartmental knee arthroplasty

The application of Artificial intelligence (AI) and machine learning (ML) tools in total (TKA) and unicompartmental knee arthroplasty (UKA) emerges with the potential to improve patient-centered decision-making and outcome prediction in orthopedics, as ML algorithms can generate patient-specific risk models. This review aims to evaluate the potential of the application of AI/ML models in the prediction of TKA outcomes and the identification of populations at risk.An extensive search in the following databases: MEDLINE, Scopus, Cinahl, Google Scholar, and EMBASE was conducted using the PIOS approach to formulate the research question. The PRISMA guideline was used for reporting the evidence of the data extracted. A modified eight-item MINORS checklist was employed for the quality assessment. The databases were screened from the inception to June 2022.Forty-four out of the 542 initially selected articles were eligible for the data analysis; 5 further articles were identified and added to the review from the PUBMED database, for a total of 49 articles included. A total of 2,595,780 patients were identified, with an overall average age of the patients of 70.2 years ± 7.9 years old. The five most common AI/ML models identified in the selected articles were: RF, in 38.77% of studies; GBM, in 36.73% of studies; ANN in 34.7% of articles; LR, in 32.65%; SVM in 26.53% of articles.This systematic review evaluated the possible uses of AI/ML models in TKA, highlighting their potential to lead to more accurate predictions, less time-consuming data processing, and improved decision-making, all while minimizing user input bias to provide risk-based patient-specific care.

ChatGPT Provides Unsatisfactory Responses to Frequently Asked Questions Regarding Anterior Cruciate Ligament Reconstruction

PURPOSE

To determine whether the free online artificial intelligence platform ChatGPT could accurately, adequately, and appropriately answer questions regarding anterior cruciate ligament (ACL) reconstruction surgery.

METHODS

A list of 10 questions about ACL surgery was created based on a review of frequently asked questions that appeared on websites of various orthopaedic institutions. Each question was separately entered into ChatGPT (version 3.5), and responses were recorded, scored, and graded independently by 3 authors. The reading level of the ChatGPT response was calculated using the WordCalc software package, and readability was assessed using the Flesch-Kincaid grade level, Simple Measure of Gobbledygook index, Coleman-Liau index, Gunning fog index, and automated readability index.

RESULTS

Of the 10 frequently asked questions entered into ChatGPT, 6 were deemed as unsatisfactory and requiring substantial clarification; 1, as adequate and requiring moderate clarification; 1, as adequate and requiring minor clarification; and 2, as satisfactory and requiring minimal clarification. The mean DISCERN score was 41 (inter-rater reliability, 0.721), indicating the responses to the questions were average. According to the readability assessments, a full understanding of the ChatGPT responses required 13.4 years of education, which corresponds to the reading level of a college sophomore.

CONCLUSIONS

Most of the ChatGPT-generated responses were outdated and failed to provide an adequate foundation for patients' understanding regarding their injury and treatment options. The reading level required to understand the responses was too advanced for some patients, leading to potential misunderstanding and misinterpretation of information. ChatGPT lacks the ability to differentiate and prioritize information that is presented to patients.

CLINICAL RELEVANCE

Recognizing the shortcomings in artificial intelligence platforms may equip surgeons to better set expectations and provide support for patients considering and preparing for ACL reconstruction.

A Blinded Comparison of Three Generative Artificial Intelligence Chatbots for Orthopaedic Surgery Therapeutic Questions

Objective To compare the quality of responses from three chatbots (ChatGPT, Bing Chat, and AskOE) across various orthopaedic surgery therapeutic treatment questions. Design We identified a series of treatment-related questions across a range of subspecialties in orthopaedic surgery. Questions were "identically" entered into one of three chatbots (ChatGPT, Bing Chat, and AskOE) and reviewed using a standardized rubric. Participants Orthopaedic surgery experts associated with McMaster University and the University of Toronto blindly reviewed all responses. Outcomes The primary outcomes were scores on a five-item assessment tool assessing clinical correctness, clinical completeness, safety, usefulness, and references. The secondary outcome was the reviewers' preferred response for each question. We performed a mixed effects logistic regression to identify factors associated with selecting a preferred chatbot. Results Across all questions and answers, AskOE was preferred by reviewers to a significantly greater extent than both ChatGPT (P<0.001) and Bing (P<0.001). AskOE also received significantly higher total evaluation scores than both ChatGPT (P<0.001) and Bing (P<0.001). Further regression analysis showed that clinical correctness, clinical completeness, usefulness, and references were significantly associated with a preference for AskOE. Across all responses, there were four considered as having major errors in response, with three occurring with ChatGPT and one occurring with AskOE. Conclusions Reviewers significantly preferred AskOE over ChatGPT and Bing Chat across a variety of variables in orthopaedic therapy questions. This technology has important implications in a healthcare setting as it provides access to trustworthy answers in orthopaedic surgery.

Orthopaedic surgeons display a positive outlook towards artificial intelligence: A survey among members of the AGA Society for Arthroscopy and Joint Surgery

Purpose

The purpose of this study was to evaluate the perspective of orthopaedic surgeons on the impact of artificial intelligence (AI) and to evaluate the influence of experience, workplace setting and familiarity with digital solutions on views on AI.

Methods

Orthopaedic surgeons of the AGA Society for Arthroscopy and Joint Surgery were invited to participate in an online, cross-sectional survey designed to gather information on professional background, subjective AI knowledge, opinion on the future impact of AI, openness towards different applications of AI, and perceived advantages and disadvantages of AI. Subgroup analyses were performed to examine the influence of experience, workplace setting and openness towards digital solutions on perspectives towards AI.

Results

Overall, 360 orthopaedic surgeons participated. The majority indicated average (43.6%) or rudimentary (38.1%) AI knowledge. Most (54.5%) expected AI to substantially influence orthopaedics within 5-10 years, predominantly as a complementary tool (91.1%). Preoperative planning (83.8%) was identified as the most likely clinical use case. A lack of consensus was observed regarding acceptable error levels. Time savings in preoperative planning (62.5%) and improved documentation (81%) were identified as notable advantages while declining skills of the next generation (64.5%) were rated as the most substantial drawback. There were significant differences in subjective AI knowledge depending on participants' experience (p = 0.021) and familiarity with digital solutions (p < 0.001), acceptable error levels depending on workplace setting (p = 0.004), and prediction of AI impact depending on familiarity with digital solutions (p < 0.001).

Conclusion

The majority of orthopaedic surgeons in this survey anticipated a notable positive impact of AI on their field, primarily as an assistive technology. A lack of consensus on acceptable error levels of AI and concerns about declining skills among future surgeons were observed.

Level of Evidence

Level IV, cross-sectional study.

-A machine learning model to predict surgical site infection after surgery of lower extremity fractures

PURPOSE

This study aimed to develop machine learning algorithms for identifying predictive factors associated with the risk of postoperative surgical site infection in patients with lower extremity fractures.

METHODS

A machine learning analysis was conducted on a dataset comprising 1,579 patients who underwent surgical fixation for lower extremity fractures to create a predictive model for risk stratification of postoperative surgical site infection. We evaluated different clinical and demographic variables to train four machine learning models (neural networks, boosted generalised linear model, naïve bayes, and penalised discriminant analysis). Performance was measured by the area under the curve score, Youdon's index and Brier score. A multivariate adaptive regression splines (MARS) was used to optimise predictor selection.

RESULTS

The final model consisted of five predictors. (1) Operating room time, (2) ankle region, (3) open injury, (4) body mass index, and (5) age. The best-performing machine learning algorithm demonstrated a promising predictive performance, with an area under the ROC curve, Youdon's index, and Brier score of 77.8%, 62.5%, and 5.1%-5.6%, respectively.

CONCLUSION

The proposed predictive model not only assists surgeons in determining high-risk factors for surgical site infections but also empowers patients to closely monitor these factors and take proactive measures to prevent complications. Furthermore, by considering the identified predictors, this model can serve as a reference for implementing preventive measures and reducing postoperative complications, ultimately enhancing patient outcomes. However, further investigations involving larger datasets and external validations are required to confirm the reliability and applicability of our model.

Supervised machine learning for the prediction of post-operative clinical outcomes of hip and knee replacements: a review

Prediction models are being increasingly used in the medical field to identify risk factors and possible outcomes. Some of these are presently being used to develop guidelines for improving clinical practice. The application of machine learning (ML), comprising a powerful set of computational tools for analysing data, has been clearly expanding in the role of predictive modelling. This paper reviews the latest developments of supervised ML techniques that have been used to analyse data related to post-operative total hip and knee replacements. The aim was to review the most recent findings of relevant published studies by outlining the methodologies employed (most-widely used supervised ML techniques), data sources, domains, limitations of predictive analytics and the quality of predictions.

Machine learning can predict anterior elevation after reverse total shoulder arthroplasty: A new tool for daily outpatient clinic?

The aim of the present study was to individuate and compare specific machine learning algorithms that could predict postoperative anterior elevation score after reverse shoulder arthroplasty surgery at different time points. Data from 105 patients who underwent reverse shoulder arthroplasty at the same institute have been collected with the purpose of generating algorithms which could predict the target. Twenty-eight features were extracted and applied to two different machine learning techniques: Linear regression and support vector regression (SVR). These two techniques were also compared in order to define to most faithfully predictive. Using the extracted features, the SVR algorithm resulted in a mean absolute error (MAE) of 11.6° and a classification accuracy (PCC) of 0.88 on the test-set. Linear regression, instead, resulted in a MAE of 13.0° and a PCC of 0.85 on the test-set. Our machine learning study demonstrates that machine learning could provide high predictive algorithms for anterior elevation after reverse shoulder arthroplasty. The differential analysis between the utilized techniques showed higher accuracy in prediction for the support vector regression. Level of Evidence III: Retrospective cohort comparison; Computer Modeling.

Using Google web search to analyze and evaluate the application of ChatGPT in femoroacetabular impingement syndrome

Background

Chat Generative Pre-trained Transformer (ChatGPT) is a new machine learning tool that allows patients to access health information online, specifically compared to Google, the most commonly used search engine in the United States. Patients can use ChatGPT to better understand medical issues. This study compared the two search engines based on: (i) frequently asked questions (FAQs) about Femoroacetabular Impingement Syndrome (FAI), (ii) the corresponding answers to these FAQs, and (iii) the most FAQs yielding a numerical response.

Purpose

To assess the suitability of ChatGPT as an online health information resource for patients by replicating their internet searches.

Study design

Cross-sectional study.

Methods

The same keywords were used to search the 10 most common questions about FAI on both Google and ChatGPT. The responses from both search engines were recorded and analyzed.

Results

Of the 20 questions, 8 (40%) were similar. Among the 10 questions searched on Google, 7 were provided by a medical practice. For numerical questions, there was a notable difference in answers between Google and ChatGPT for 3 out of the top 5 most common questions (60%). Expert evaluation indicated that 67.5% of experts were satisfied or highly satisfied with the accuracy of ChatGPT's descriptions of both conservative and surgical treatment options for FAI. Additionally, 62.5% of experts were satisfied or highly satisfied with the safety of the information provided. Regarding the etiology of FAI, including cam and pincer impingements, 52.5% of experts expressed satisfaction or high satisfaction with ChatGPT's explanations. Overall, 62.5% of experts affirmed that ChatGPT could serve effectively as a reliable medical resource for initial information retrieval.

Conclusion

This study confirms that ChatGPT, despite being a new tool, shows significant potential as a supplementary resource for health information on FAI. Expert evaluations commend its capacity to provide accurate and comprehensive responses, valued by medical professionals for relevance and safety. Nonetheless, continuous improvements in its medical content's depth and precision are recommended for ongoing reliability. While ChatGPT offers a promising alternative to traditional search engines, meticulous validation is imperative before it can be fully embraced as a trusted medical resource.

Application of Artificial Intelligence in Shoulder Pathology

Artificial intelligence (AI) refers to the science and engineering of creating intelligent machines for imitating and expanding human intelligence. Given the ongoing evolution of the multidisciplinary integration trend in modern medicine, numerous studies have investigated the power of AI to address orthopedic-specific problems. One particular area of investigation focuses on shoulder pathology, which is a range of disorders or abnormalities of the shoulder joint, causing pain, inflammation, stiffness, weakness, and reduced range of motion. There has not yet been a comprehensive review of the recent advancements in this field. Therefore, the purpose of this review is to evaluate current AI applications in shoulder pathology. This review mainly summarizes several crucial stages of the clinical practice, including predictive models and prognosis, diagnosis, treatment, and physical therapy. In addition, the challenges and future development of AI technology are also discussed.

Bibliometric analysis of machine learning trends and hotspots in arthroplasty literature over 31 years

Background

Artificial intelligence has demonstrated utility in orthopedic research. Algorithmic models derived from machine learning have demonstrated adaptive learning with predictive application towards outcomes, leading to increased traction in the literature. This study aims to identify machine learning arthroplasty research trends and anticipate emerging key terms.

Methods

Published literature focused on machine learning in arthroplasty from 1992 to 2023 was selected through the Web of Science Core Collection of Clarivate Analytics. Following that, bibliometric indicators were attained and brought in to perform an additional examination using Bibliometrix and VOSviewer to identify historical and present patterns within the literature.

Results

A total of 235 documents were obtained through bibliometric sourcing based on machine learning applications within the arthroplasty literature. Thirty-four countries published articles on the topic, and the United States was demonstrated to be the largest global contributor. Four hundred-five institutions internationally contributed articles, with Harvard Medical School and the University of California system as the most relevant institutes, with 75 and 44 articles produced, respectively. Kwon YM was the most productive author, while Haeberle HS and Ramkumar PN were the most impactful based on h-index. The Thematic map and Co-occurrence visualization helped identify both major and niche themes present in the scientific databases.

Conclusions

Machine learning in arthroplasty research continues to gain traction with a growing annual production rate and contributions from international authors and institutions. Institutions and authors based in the United States are the leading contributors to machine learning applications within arthroplasty research. This research discerns trends that have occurred, are presently ongoing, and are emerging within this field, aiming to inform future hotspot development.

Chat Generative Pretrained Transformer (ChatGPT) and Bard: Artificial Intelligence Does not yet Provide Clinically Supported Answers for Hip and Knee Osteoarthritis

BACKGROUND

Advancements in artificial intelligence (AI) have led to the creation of large language models (LLMs), such as Chat Generative Pretrained Transformer (ChatGPT) and Bard, that analyze online resources to synthesize responses to user queries. Despite their popularity, the accuracy of LLM responses to medical questions remains unknown. This study aimed to compare the responses of ChatGPT and Bard regarding treatments for hip and knee osteoarthritis with the American Academy of Orthopaedic Surgeons (AAOS) Evidence-Based Clinical Practice Guidelines (CPGs) recommendations.

METHODS

Both ChatGPT (Open AI) and Bard (Google) were queried regarding 20 treatments (10 for hip and 10 for knee osteoarthritis) from the AAOS CPGs. Responses were classified by 2 reviewers as being in "Concordance," "Discordance," or "No Concordance" with AAOS CPGs. A Cohen's Kappa coefficient was used to assess inter-rater reliability, and Chi-squared analyses were used to compare responses between LLMs.

RESULTS

Overall, ChatGPT and Bard provided responses that were concordant with the AAOS CPGs for 16 (80%) and 12 (60%) treatments, respectively. Notably, ChatGPT and Bard encouraged the use of non-recommended treatments in 30% and 60% of queries, respectively. There were no differences in performance when evaluating by joint or by recommended versus non-recommended treatments. Studies were referenced in 6 (30%) of the Bard responses and none (0%) of the ChatGPT responses. Of the 6 Bard responses, studies could only be identified for 1 (16.7%). Of the remaining, 2 (33.3%) responses cited studies in journals that did not exist, 2 (33.3%) cited studies that could not be found with the information given, and 1 (16.7%) provided links to unrelated studies.

CONCLUSIONS

Both ChatGPT and Bard do not consistently provide responses that align with the AAOS CPGs. Consequently, physicians and patients should temper expectations on the guidance AI platforms can currently provide.

Predicting conversion of ambulatory ACDF patients to inpatient: a machine learning approach

BACKGROUND CONTEXT

Machine learning is a powerful tool that has become increasingly important in the orthopedic field. Recently, several studies have reported that predictive models could provide new insights into patient risk factors and outcomes. Anterior cervical discectomy and fusion (ACDF) is a common operation that is performed as an outpatient procedure. However, some patients are required to convert to inpatient status and prolonged hospitalization due to their condition. Appropriate patient selection and identification of risk factors for conversion could provide benefits to patients and the use of medical resources.

PURPOSE

This study aimed to develop a machine-learning algorithm to identify risk factors associated with unplanned conversion from outpatient to inpatient status for ACDF patients.

STUDY DESIGN/SETTING

This is a machine-learning-based analysis using retrospectively collected data.

PATIENT SAMPLE

Patients who underwent one- or two-level ACDF in an ambulatory setting at a single specialized orthopedic hospital between February 2016 to December 2021.

OUTCOME MEASURES

Length of stay, conversion rates from ambulatory setting to inpatient.

METHODS

Patients were divided into two groups based on length of stay: (1) Ambulatory (discharge within 24 hours) or Extended Stay (greater than 24 hours but fewer than 48 hours), and (2) Inpatient (greater than 48 hours). Factors included in the model were based on literature review and clinical expertise. Patient demographics, comorbidities, and intraoperative factors, such as surgery duration and time, were included. We compared the performance of different machine learning algorithms: Logistic Regression, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). We split the patient data into a training and validation dataset using a 70/30 split. The different models were trained in the training dataset using cross-validation. The performance was then tested in the unseen validation set. This step is important to detect overfitting. The performance was evaluated using the area under the curve (AUC) of the receiver operating characteristics analysis (ROC) as the primary outcome. An AUC of 0.7 was considered fair, 0.8 good, and 0.9 excellent, according to established cut-offs.

RESULTS

A total of 581 patients (59% female) were available for analysis. Of those, 140 (24.1%) were converted to inpatient status. The median age was 51 (IQR 44-59), and the median BMI was 28 kg/m2 (IQR 24-32). The XGBoost model showed the best performance with an AUC of 0.79. The most important features were the length of the operation, followed by sex (based on biological attributes), age, and operation start time. The logistic regression model and the SVM showed worse results, with an AUC of 0.71 each.

CONCLUSIONS

This study demonstrated a novel approach to predicting conversion to inpatient status in eligible patients for ambulatory surgery. The XGBoost model showed good predictive capabilities, superior to the older machine learning approaches. This model also revealed the importance of surgical duration time, BMI, and age as risk factors for patient conversion. A developing field of study is using machine learning in clinical decision-making. Our findings contribute to this field by demonstrating the feasibility and accuracy of such methods in predicting outcomes and identifying risk factors, although external and multi-center validation studies are needed.

ChatGPT Can Offer Satisfactory Responses to Common Patient Questions Regarding Elbow Ulnar Collateral Ligament Reconstruction

Purpose

To determine whether ChatGPT effectively responds to 10 commonly asked questions concerning ulnar collateral ligament (UCL) reconstruction.

Methods

A comprehensive list of 90 UCL reconstruction questions was initially created, with a final set of 10 "most commonly asked" questions ultimately selected. Questions were presented to ChatGPT and its response was documented. Responses were evaluated independently by 3 authors using an evidence-based methodology, resulting in a grading system categorized as follows: (1) excellent response not requiring clarification; (2) satisfactory requiring minimal clarification; (3) satisfactory requiring moderate clarification; and (4) unsatisfactory requiring substantial clarification.

Results

Six of 10 ten responses were rated as "excellent" or "satisfactory." Of those 6 responses, 2 were determined to be "excellent response not requiring clarification," 3 were "satisfactory requiring minimal clarification," and 1 was "satisfactory requiring moderate clarification." Four questions encompassing inquiries about "What are the potential risks of UCL reconstruction surgery?" "Which type of graft should be used for my UCL reconstruction?" and "Should I have UCL reconstruction or repair?" were rated as "unsatisfactory requiring substantial clarification."

Conclusions

ChatGPT exhibited the potential to improve a patient's basic understanding of UCL reconstruction and provided responses that were deemed satisfactory to excellent for 60% of the most commonly asked questions. For the other 40% of questions, ChatGPT gave unsatisfactory responses, primarily due to a lack of relevant details or the need for further explanation.

Clinical Relevance

ChatGPT can assist in patient education regarding UCL reconstruction; however, its ability to appropriately answer more complex questions remains to be an area of skepticism and future improvement.

A predictive algorithm for perioperative complications and readmission after ankle arthrodesis

PURPOSE

Ankle arthrodesis is a mainstay of surgical management for ankle arthritis. Accurately risk-stratifying patients who undergo ankle arthrodesis would be of great utility. There is a paucity of accurate prediction models that can be used to pre-operatively risk-stratify patients for ankle arthrodesis. We aim to develop a predictive model for major perioperative complication or readmission after ankle arthrodesis.

METHODS

This is a retrospective cohort study of adult patients who underwent ankle arthrodesis at any non-federal California hospital between 2015 and 2017. The primary outcome is readmission within 30 days or major perioperative complication. We build logistic regression and ML models spanning different classes of modeling approaches, assessing discrimination and calibration. We also rank the contribution of the included variables to model performance for prediction of adverse outcomes.

RESULTS

A total of 1084 patients met inclusion criteria for this study. There were 131 patients with major complication or readmission (12.1%). The XGBoost algorithm demonstrates the highest discrimination with an area under the receiver operating characteristic curve of 0.707 and is well-calibrated. The features most important for prediction of adverse outcomes for the XGBoost model include: diabetes, peripheral vascular disease, teaching hospital status, morbid obesity, history of musculoskeletal infection, history of hip fracture, renal failure, implant complication, history of major fracture.

CONCLUSION

We report a well-calibrated algorithm for prediction of major perioperative complications and 30-day readmission after ankle arthrodesis. This tool may help accurately risk-stratify patients and decrease likelihood of major complications.

Development of a machine learning model for identifying the optimal situation favoring double-level osteotomy over single-level high tibial osteotomy

BACKGROUND

This study aimed to develop a machine learning (ML) model to identify the optimal situation wherein double-level osteotomy (DLO) is favored for severe varus knees by analyzing unfavorable outcomes. This study hypothesized that there are the most favorable algorithms and contributing factors for identifying the optimal situation favoring DLO over opening-wedge high tibial osteotomy (OWHTO).

METHODS

Data were retrospectively collected from patients who underwent OWHTO (505 knees). Unfavorable outcome parameters were defined as follows: (1) medial proximal tibial angle (MPTA) > 95°, (2) joint line convergence angle (JLCA) > 4° (insufficient medial release), (3) JLCA < 0° (medial instability), (4) recurrence of varus deformity, and (5) lateral hinge fracture. The input data for the ML model included demographic data and preoperative radiological and intra-operative factors. The ML model was used to evaluate overall and to evaluate each unfavorable outcome. Interpretation by the model was performed by SHapley Additive exPlanations.

RESULTS

The unfavorable group had a larger JLCA and MPTA preoperatively than the favorable group in the conventional comparison. The light gradient boosting machine (LGBM) demonstrated the highest AUC of 0.66 and F-1 score of 0.72 among the ML algorithms. In the overall assessment, the preoperative weight-bearing line ratio (WBLR) was the factor that contributed the most, followed by the preoperative JLCA and the ΔWBLR. ΔWBLR and the preoperative JLCA were the contributing factors for each outcome.

CONCLUSIONS

The LGBM model was superior in predicting the optimal situations favoring DLO over OWHTO. Preoperative WBLR, preoperative JLCA, and ΔWBLR significantly contributed to the unfavorable outcomes overall and for each outcome in the ML model.

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.

Future of Artificial Intelligence in Surgery: A Narrative Review

Artificial intelligence (AI) is the capability of a machine to execute cognitive processes that are typically considered to be functions of the human brain. It is the study of algorithms that enable machines to reason and perform mental tasks, including problem-solving, object and word recognition, and decision-making. Once considered science fiction, AI today is a fact and an increasingly prevalent subject in both academic and popular literature. It is expected to reshape medicine, benefiting both healthcare professionals and patients. Machine learning (ML) is a subset of AI that allows machines to learn and make predictions by recognizing patterns, thus empowering the medical team to deliver better care to patients through accurate diagnosis and treatment. ML is expanding its footprint in a variety of surgical specialties, including general surgery, ophthalmology, cardiothoracic surgery, and vascular surgery, to name a few. In recent years, we have seen AI make its way into the operating theatres. Though it has not yet been able to replace the surgeon, it has the potential to become a highly valuable surgical tool. Rest assured that the day is not far off when AI shall play a significant intraoperative role, a projection that is currently marred by safety concerns. This review aims to explore the present application of AI in various surgical disciplines and how it benefits both patients and physicians, as well as the current obstacles and limitations facing its seemingly unstoppable rise.

Systematic reviews of machine learning in healthcare: a literature review

INTRODUCTION

The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery.

METHODS

A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted.

RESULTS

In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively).

EXPERT OPINION

The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.

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.

Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature

Statement of the Problem

In recent years, the use of artificial intelligence (AI) has become increasingly popular in dentistry because it facilitates the process of diagnosis and clinical decision-making. However, AI holds multiple prominent drawbacks, which restrict its wide application today. It is necessary for dentists to be aware of AI's pros and cons before its implementation.

Purpose

Therefore, the present study was conducted to comprehensively review various applications of AI in all dental branches along with its advantages and disadvantages.

Materials and Method

For this review article, a complete query was carried out on PubMed and Google Scholar databases and the studies published during 2010-2022 were collected using the keywords "Artificial Intelligence", "Dentistry," "Machine learning," "Deep learning," and "Diagnostic System." Ultimately, 116 relevant articles focused on artificial intelligence in dentistry were selected and evaluated.

Results

In new research AI applications in detecting dental abnormalities and oral malignancies based on radiographic view and histopathological features, designing dental implants and crowns, determining tooth preparation finishing line, analyzing growth patterns, estimating biological age, predicting the viability of dental pulp stem cells, analyzing the gene expression of periapical lesions, forensic dentistry, and predicting the success rate of treatments, have been mentioned. Despite AI's benefits in clinical dentistry, three controversial challenges including ease of use, financial return on investment, and evidence of performance exist and need to be managed.

Conclusion

As evidenced by the obtained results, the most crucial progression of AI is in oral malignancies' diagnostic systems. However, AI's newest advancements in various branches of dentistry require further scientific work before being applied to clinical practice. Moreover, the immense use of AI in clinical dentistry is only achievable when its challenges are appropriately managed.

Imaging of knee osteoarthritis: a review of multimodal diagnostic approach

Knee osteoarthritis (KOA) is a common chronic condition among the elderly population that significantly affects the quality of life. Imaging is crucial in the diagnosis, evaluation, and management of KOA. This manuscript reviews the various imaging modalities available until now, with a little focus on the recent developments with Artificial Intelligence. Currently, radiography is the first-line imaging modality recommended for the diagnosis of KOA, owing to its wide availability, affordability, and ability to provide a clear view of bony components of the knee. Although radiography is useful in assessing joint space narrowing (JSN), osteophytes and subchondral sclerosis, it has limited effectiveness in detecting early cartilage damage, soft tissue abnormalities and synovial inflammation. Ultrasound is a safe and affordable imaging technique that can provide information on cartilage thickness, synovial fluid, JSN and osteophytes, though its ability to evaluate deep structures such as subchondral bone is limited. Magnetic resonance imaging (MRI) represents the optimal imaging modality to assess soft tissue structures. New MRI techniques are able to detect early cartilage damage measuring the T1ρ and T2 relaxation time of knee cartilage. Delayed gadolinium-enhanced MRI of cartilage, by injecting a contrast agent to enhance the visibility of the cartilage on MRI scans, can provide information about its integrity. Despite these techniques can provide valuable information about the biochemical composition of knee cartilage and can help detect early signs of osteoarthritis (OA), they may not be widely available. Computed tomography (CT) has restricted utility in evaluating OA; nonetheless, weight-bearing CT imaging, using the joint space mapping technique, exhibits potential in quantifying knee joint space width and detecting structural joint ailments. PET-MRI is a hybrid imaging technique able to combine morphological information on bone and soft tissue alterations with the biochemical changes, but more research is needed to justify its high cost and time involved. The new tools of artificial intelligence, including machine learning models, can assist in detecting patterns and correlations in KOA that may be useful in the diagnosis, grading, predicting the need for arthroplasty, and improving surgical accuracy.

Machine Learning Models Based on a National-Scale Cohort Identify Patients at High Risk for Prolonged Lengths of Stay Following Primary Total Hip Arthroplasty

BACKGROUND

Existing machine learning models that predicted prolonged lengths of stay (LOS) following primary total hip arthroplasty (THA) were limited by the small training volume and exclusion of important patient factors. This study aimed to develop machine learning models using a national-scale data set and examine their performance in predicting prolonged LOS following THA.

METHODS

A total of 246,265 THAs were analyzed from a large database. Prolonged LOS was defined as exceeding the 75th percentile of all LOSs in the cohort. Candidate predictors of prolonged LOS were selected by recursive feature elimination and used to construct four machine learning models-artificial neural network, random forest, histogram-based gradient boosting, and k-nearest neighbor. The model performance was assessed by discrimination, calibration, and utility.

RESULTS

All models exhibited excellent performance in discrimination (area under the receiver operating characteristic curve [AUC] = 0.72 to 0.74) and calibration (slope: 0.83 to 1.18, intercept: -0.01 to 0.11, Brier score: 0.185 to 0.192) during both training and testing sessions. The artificial neural network was the best performer with an AUC of 0.73, calibration slope of 0.99, calibration intercept of -0.01, and Brier score of 0.185. All models showed great utility by producing higher net benefits than the default treatment strategies in the decision curve analyses. Age, laboratory tests, and surgical variables were the strongest predictors of prolonged LOS.

CONCLUSION

The excellent prediction performance of machine learning models demonstrated their capacity to identify patients prone to prolonged LOS. Many factors contributing to prolonged LOS can be optimized to minimize hospital stay for high-risk patients.

An Overview of Machine Learning in Orthopedic Surgery: An Educational Paper

The growth of artificial intelligence combined with the collection and storage of large amounts of data in the electronic medical record collection has created an opportunity for orthopedic research and translation into the clinical environment. Machine learning (ML) is a type of artificial intelligence tool well suited for processing the large amount of available data. Specific areas of ML frequently used by orthopedic surgeons performing total joint arthroplasty include tabular data analysis (spreadsheets), medical imaging processing, and natural language processing (extracting concepts from text). Previous studies have discussed models able to identify fractures in radiographs, identify implant type in radiographs, and determine the stage of osteoarthritis based on walking analysis. Despite the growing popularity of ML, there are limitations including its reliance on "good" data, potential for overfitting, long life cycle for creation, and ability to only perform one narrow task. This educational article will further discuss a general overview of ML, discussing these challenges and including examples of successfully published models.

Artifical intelligence use in orthopedics: an ethical point of view

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.

Artificial intelligence and ChatGPT in Orthopaedics and sports medicine

Artificial intelligence (AI) is looked upon nowadays as the potential major catalyst for the fourth industrial revolution. In the last decade, AI use in Orthopaedics increased approximately tenfold. Artificial intelligence helps with tracking activities, evaluating diagnostic images, predicting injury risk, and several other uses. Chat Generated Pre-trained Transformer (ChatGPT), which is an AI-chatbot, represents an extremely controversial topic in the academic community. The aim of this review article is to simplify the concept of AI and study the extent of AI use in Orthopaedics and sports medicine literature. Additionally, the article will also evaluate the role of ChatGPT in scientific research and publications.Level of evidence: Level V, letter to review.

Using a Google Web Search Analysis to Assess the Utility of ChatGPT in Total Joint Arthroplasty

BACKGROUND

Rapid technological advancements have laid the foundations for the use of artificial intelligence in medicine. The promise of machine learning (ML) lies in its potential ability to improve treatment decision making, predict adverse outcomes, and streamline the management of perioperative healthcare. In an increasing consumer-focused health care model, unprecedented access to information may extend to patients using ChatGPT to gain insight into medical questions. The main objective of our study was to replicate a patient's internet search in order to assess the appropriateness of ChatGPT, a novel machine learning tool released in 2022 that provides dialogue responses to queries, in comparison to Google Web Search, the most widely used search engine in the United States today, as a resource for patients for online health information. For the 2 different search engines, we compared i) the most frequently asked questions (FAQs) associated with total knee arthroplasty (TKA) and total hip arthroplasty (THA) by question type and topic; ii) the answers to the most frequently asked questions; as well as iii) the FAQs yielding a numerical response.

METHODS

A Google web search was performed with the following search terms: "total knee replacement" and "total hip replacement." These terms were individually entered and the first 10 FAQs were extracted along with the source of the associated website for each question. The following statements were inputted into ChatGPT: 1) "Perform a google search with the search term 'total knee replacement' and record the 10 most FAQs related to the search term" as well as 2) "Perform a google search with the search term 'total hip replacement' and record the 10 most FAQs related to the search term." A Google web search was repeated with the same search terms to identify the first 10 FAQs that included a numerical response for both "total knee replacement" and "total hip replacement." These questions were then inputted into ChatGPT and the questions and answers were recorded.

RESULTS

There were 5 of 20 (25%) questions that were similar when performing a Google web search and a search of ChatGPT for all search terms. Of the 20 questions asked for the Google Web Search, 13 of 20 were provided by commercial websites. For ChatGPT, 15 of 20 (75%) questions were answered by government websites, with the most frequent one being PubMed. In terms of numerical questions, 11 of 20 (55%) of the most FAQs provided different responses between a Google web search and ChatGPT.

CONCLUSION

A comparison of the FAQs by a Google web search with attempted replication by ChatGPT revealed heterogenous questions and responses for open and discrete questions. ChatGPT should remain a trending use as a potential resource to patients that needs further corroboration until its ability to provide credible information is verified and concordant with the goals of the physician and the patient alike.

[Evidence-based versus expertise-based medicine in orthopedic and trauma surgery : There is nothing more practical than a good theory]

About a quarter of a century after the introduction of the concept and principles of evidence-based medicine (EbM), some healthcare providers are still adamant that these are incompatible with knowledge gained through experience. Across the surgical disciplines, it is often argued EbM underestimates or neglects the importance of intuition and surgical skills. To put it bluntly, these assumptions are wrong and often characterized by a misunderstanding of the methodology of EbM. Even the best controlled trial cannot be properly interpreted or implemented without clinical reasoning; furthermore, clinicians of all disciplines are obligated to provide care according to the current state of scientific knowledge. In an era of revolutionary biomedical developments, exponential increase of research but incremental innovations, they must become familiar with pragmatic tools to appraise the validity and relevance of clinical study results, and to decide whether there is a need to adapt current beliefs and practices based on the new information. We herein use the recent example of a new medical device for the surgical treatment of rotator cuff tears and subacromial impingement syndrome to illustrate how important it is to interpret data in the context of a precise, answerable question and to combine clinical expertise with methodological principles offered by EbM.

Predicting Corrosion Damage in the Human Body Using Artificial Intelligence: In Vitro Progress and Future Applications

Artificial intelligence (AI) is used in the clinic to improve patient care. While the successes illustrate AI's impact, few studies have led to improved clinical outcomes. In this review, we focus on how AI models implemented in nonorthopedic fields of corrosion science may apply to the study of orthopedic alloys. We first define and introduce fundamental AI concepts and models, as well as physiologically relevant corrosion damage modes. We then systematically review the corrosion/AI literature. Finally, we identify several AI models that may be implemented to study fretting, crevice, and pitting corrosion of titanium and cobalt chrome alloys.

Artificial Intelligence in Musculoskeletal Conditions

Artificial intelligence (AI) refers to computer capabilities that resemble human intelligence. AI implies the ability to learn and perform tasks that have not been specifically programmed. Moreover, it is an iterative process involving the ability of computerized systems to capture information, transform it into knowledge, and process it to produce adaptive changes in the environment. A large labeled database is needed to train the AI system and generate a robust algorithm. Otherwise, the algorithm cannot be applied in a generalized way. AI can facilitate the interpretation and acquisition of radiological images. In addition, it can facilitate the detection of trauma injuries and assist in orthopedic and rehabilitative processes. The applications of AI in musculoskeletal conditions are promising and are likely to have a significant impact on the future management of these patients.

Approaching Artificial Intelligence in Orthopaedics: Predictive Analytics and Machine Learning to Prognosticate Arthroscopic Rotator Cuff Surgical Outcomes

Machine learning (ML) has not yet been used to identify factors predictive for post-operative functional outcomes following arthroscopic rotator cuff repair (ARCR). We propose a novel algorithm to predict ARCR outcomes using machine learning. This is a retrospective cohort study from a prospectively collected database. Data were collected from the Surgical Outcome System Global Registry (Arthrex, Naples, FL, USA). Pre-operative and 3-month, 6-month, and 12-month post-operative American Shoulder and Elbow Surgeons (ASES) scores were collected and used to develop a ML model. Pre-operative factors including demography, comorbidities, cuff tear, tissue quality, and fixation implants were fed to the ML model. The algorithm then produced an expected post-operative ASES score for each patient. The ML-produced scores were compared to actual scores using standard test-train machine learning principles. Overall, 631 patients who underwent shoulder arthroscopy from January 2011 to March 2020 met inclusion criteria for final analysis. A substantial number of the test dataset predictions using the XGBoost algorithm were within the minimal clinically important difference (MCID) and substantial clinical benefit (SCB) thresholds: 67% of the 12-month post-operative predictions were within MCID, while 84% were within SCB. Pre-operative ASES score, pre-operative pain score, body mass index (BMI), age, and tendon quality were the most important features in predicting patient recovery as identified using Shapley additive explanations (SHAP). In conclusion, the proposed novel machine learning algorithm can use pre-operative factors to predict post-operative ASES scores accurately. This can further supplement pre-operative counselling, planning, and resource allocation. Level of Evidence: III.

Classification and prediction of spinal disease based on the SMOTE-RFE-XGBoost model

Spinal diseases are killers that cause long-term disturbance to people with complex and diverse symptoms and may cause other conditions. At present, the diagnosis and treatment of the main diseases mainly depend on the professional level and clinical experience of doctors, which is a breakthrough problem in the field of medicine. This article proposes the SMOTE-RFE-XGBoost model, which takes the physical angle of human bone as the research index for feature selection and classification model construction to predict spinal diseases. The research process is as follows: two groups of people with normal and abnormal spine conditions are taken as the research objects of this article, and the synthetic minority oversampling technique (SMOTE) algorithm is used to address category imbalance. Three methods, least absolute shrinkage and selection operator (LASSO), tree-based feature selection, and recursive feature elimination (RFE), are used for feature selection. Logistic regression (LR), support vector machine (SVM), parsimonious Bayes, decision tree (DT), random forest (RF), gradient boosting tree (GBT), extreme gradient boosting (XGBoost), and ridge regression models are used to classify the samples, construct single classification models and combine classification models and rank the feature importance. According to the accuracy and mean square error (MSE) values, the SMOTE-RFE-XGBoost combined model has the best classification, with accuracy, MSE and F1 values of 97.56%, 0.1111 and 0.8696, respectively. The importance of four indicators, lumbar slippage, cervical tilt, pelvic radius and pelvic tilt, was higher.

Artificial Intelligence Radiographic Analysis Tool for Total Knee Arthroplasty

BACKGROUND

The postoperative follow-up of a patient after total knee arthroplasty (TKA) requires regular evaluation of the condition of the knee through interpretation of X-rays. This rigorous analysis requires expertize, time, and methodical standardization. Our work evaluated the use of an artificial intelligence tool, X-TKA, to assist surgeons in their interpretation.

METHODS

A series of 12 convolutional neural networks were trained on a large database containing 39,751 X-ray images. These algorithms are able to determine examination quality, identify image characteristics, assess prosthesis sizing and positioning, measure knee-prosthesis alignment angles, and detect anomalies in the bone-cement-implant complex. The individual interpretations of a pool of senior surgeons with and without the assistance of X-TKA were evaluated on a reference dataset built in consensus by senior surgeons.

RESULTS

The algorithms obtained a mean area under the curve value of 0.98 on the quality assurance and the image characteristics tasks. They reached a mean difference for the predicted angles of 1.71° (standard deviation, 1.53°), similar to the surgeon average difference of 1.69° (standard deviation, 1.52°). The comparative analysis showed that the assistance of X-TKA allowed surgeons to gain 5% in accuracy and 12% in sensitivity in the detection of interface anomalies. Moreover, this study demonstrated a gain in repeatability for each single surgeon (Light's kappa +0.17), as well as a gain in the reproducibility between surgeons (Light's kappa +0.1).

CONCLUSION

This study highlights the benefit of using an intelligent artificial tool for a standardized interpretation of postoperative knee X-rays and indicates the potential for its use in clinical practice.

Orthopedic surgeons’ attitudes and expectations toward artificial intelligence: A national survey study

Background/Aim: There is a lack of understanding of artificial intelligence (AI) among orthopedic surgeons regarding how it can be used in their clinical practices. This study aimed to evaluate the attitudes of orthopedic surgeons regarding the application of AI in their practices. Methods: A cross-sectional study was conducted in Turkey among 189 orthopedic surgeons between November 2021 and February 2022. An electronic survey was designed using the SurveyMonkey platform. The questionnaire included six subsections related to AI usefulness in clinical practice and participants’ knowledge about the topic. It also surveyed their acceptance level of learning, concerns about the potential risks of AI, and implementation of this technology into their daily practice Results: A total of 33.9% of the participants indicated that they were familiar with the concept of AI, while 82.5% planned to learn about artificial intelligence in the coming years. Most of the surgeons (68.3%) reported not using AI in their daily practice. The activities of orthopedic associations focused on AI were insufficient according to 77.2% of participants. Orthopedic surgeons expressed concern over AI involvement in the future regarding an insensitive and nonempathic attitude toward the patient (53.5%). A majority of respondents (80.4%) indicated that AI was most feasible in extremity reconstruction. Pelvis fractures were found in the region where the AI system is most needed in the fracture classification (68.7%). Conclusion: Most of the respondents did not use AI in their daily clinical practice; however, almost all surgeons had plans to learn about artificial intelligence in the future. There was a need to improve orthopedic associations’ activities focusing on artificial intelligence. Furthermore, new research including the medical ethics issues of the field will be needed to allay the surgeons’ worries. The classification system of pelvic fractures and sub-branches of orthopedic extremity reconstruction were the most feasible areas for AI systems. We believe that this study will serve as a guide for all branches of orthopedic medicine.

Digital Health Applications to Establish a Remote Diagnosis of Orthopedic Knee Disorders: Scoping Review

BACKGROUND

Knee pain is highly prevalent worldwide, and this number is expected to rise in the future. The COVID-19 outbreak, in combination with the aging population, rising health care costs, and the need to make health care more accessible worldwide, has led to an increasing demand for digital health care applications to deliver care for patients with musculoskeletal conditions. Digital health and other forms of telemedicine can add value in optimizing health care for patients and health care providers. This might reduce health care costs and make health care more accessible while maintaining a high level of quality. Although expectations are high, there is currently no overview comparing digital health applications with face-to-face contact in clinical trials to establish a primary knee diagnosis in orthopedic surgery.

OBJECTIVE

This study aimed to investigate the currently available digital health and telemedicine applications to establish a primary knee diagnosis in orthopedic surgery in the general population in comparison with imaging or face-to-face contact between patients and physicians.

METHODS

A scoping review was conducted using the PubMed and Embase databases according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) statement. The inclusion criteria were studies reporting methods to determine a primary knee diagnosis in orthopedic surgery using digital health or telemedicine. On April 28 and 29, 2021, searches were conducted in PubMed (MEDLINE) and Embase. Data charting was conducted using a predefined form and included details on general study information, study population, type of application, comparator, analyses, and key findings. A risk-of-bias analysis was not deemed relevant considering the scoping review design of the study.

RESULTS

After screening 5639 articles, 7 (0.12%) were included. In total, 2 categories to determine a primary diagnosis were identified: screening studies (4/7, 57%) and decision support studies (3/7, 43%). There was great heterogeneity in the included studies in algorithms used, disorders, input parameters, and outcome measurements. No more than 25 knee disorders were included in the studies. The included studies showed a relatively high sensitivity (67%-91%). The accuracy of the different studies was generally lower, with a specificity of 27% to 48% for decision support studies and 73% to 96% for screening studies.

CONCLUSIONS

This scoping review shows that there are a limited number of available applications to establish a remote diagnosis of knee disorders in orthopedic surgery. To date, there is limited evidence that digital health applications can assist patients or orthopedic surgeons in establishing the primary diagnosis of knee disorders. Future research should aim to integrate multiple sources of information and a standardized study design with close collaboration among clinicians, data scientists, data managers, lawyers, and service users to create reliable and secure databases.

Global research landscape on artificial intelligence in arthroplasty: A bibliometric analysis

Background

Artificial intelligence (AI) has promising applications in arthroplasty. In response to the knowledge explosion resulting from the rapid growth of publications, we applied bibliometric analysis to explore the research profile and topical trends in this field.

Methods

The articles and reviews related to AI in arthroplasty were retrieved from 2000 to 2021. The Java-based Citespace, VOSviewer, R software-based Bibiometrix, and an online platform systematically evaluated publications by countries, institutions, authors, journals, references, and keywords.

Results

A total of 867 publications were included. Over the past 22 years, the number of AI-related publications in the field of arthroplasty has grown exponentially. The United States was the most productive and academically influential country. The Cleveland Clinic was the most prolific institution. Most publications were published in high academic impact journals. However, collaborative networks revealed a lack and imbalance of inter-regional, inter-institutional, and inter-author cooperation. Two emerging research areas represented the development trends: major AI subfields such as machine learning and deep learning, and the other is research related to clinical outcomes.

Conclusion

AI in arthroplasty is evolving rapidly. Collaboration between different regions and institutions should be strengthened to deepen our understanding further and exert critical implications for decision-making. Predicting clinical outcomes of arthroplasty using novel AI strategies may be a promising application in this field.

The Clinical Application of Machine Learning Models for Risk Analysis of Ramp Lesions in Anterior Cruciate Ligament Injuries

BACKGROUND

Peripheral tears of the posterior horn of the medial meniscus, known as "ramp lesions," are commonly found in anterior cruciate ligament (ACL)-deficient knees but are frequently missed on routine evaluation.

PURPOSE

To predict the presence of ramp lesions in ACL-deficient knees using machine learning methods with associated risk factors.

STUDY DESIGN

Cohort study (Diagnosis); Level of evidence, 2.

METHODS

This study included 362 patients who underwent ACL reconstruction between June 2010 and March 2019. The exclusion criteria were combined fractures and multiple ligament injuries, except for medial collateral ligament injuries. Patients were grouped according to the presence of ramp lesions on arthroscopic surgery. Binary logistic regression was used to analyze risk factors including age, sex, body mass index, time from injury to surgery (≥3 or <3 months), mechanism of injury (contact or noncontact), side-to-side laxity, pivot-shift grade, medial and lateral tibial/meniscal slope, location of bone contusion, mechanical axis angle, and lateral femoral condyle (LFC) ratio. The receiver operating characteristic curve and area under the curve were also evaluated.

RESULTS

Ramp lesions were identified in 112 patients (30.9%). The risk for ramp lesions increased with steeper medial tibial and meniscal slopes, higher knee laxity, and an increased LFC ratio. Comparing the final performance of all models, the random forest model yielded the best performance (area under the curve: 0.944), although there were no significant differences among the models (P > .05). The cut-off values for the presence of ramp lesions on receiver operating characteristic analysis were as follows: medial tibial slope >5.5° (P < .001), medial meniscal slope >5.0° (P < .001), and LFC ratio >71.3% (P = .033).

CONCLUSION

Steep medial tibial and meniscal slopes, an increased LFC ratio, and higher knee rotatory laxity were observed risk factors for ramp lesions in patients with an ACL injury. The prediction model of this study could be used as a supplementary diagnostic tool for ramp lesions in ACL-injured knees. In general, care should be taken in patients with ramp lesions and its risk factors during ACL reconstruction.

Machine Learning Model Identifies Preoperative Opioid Use, Male Sex, and Elevated BMI as Predictive Factors for of Prolonged Opioid Consumption Following Arthroscopic Meniscal Surgery

PURPOSE

To develop a predictive machine learning model to identify prognostic factors for continued opioid prescriptions after arthroscopic meniscus surgery.

METHODS

Patients undergoing arthroscopic meniscal surgery, such as meniscus debridement, repair, or revision at a single institution from 2013 to 2017 were retrospectively followed up to 1 year postoperatively. Procedural details were recorded, including concomitant procedures, primary versus revision, and whether a partial debridement or a repair was performed. Intraoperative arthritis severity was measured using the Outerbridge Classification. The number of opioid prescriptions in each month was recorded. Primary analysis used was the multivariate Cox-Regression model. We then created a naïve Bayesian model, a machine learning classifier that uses Bayes' theorem with an assumption of independence between variables.

RESULTS

A total of 581 patients were reviewed. Postoperative opioid refills occurred in 98 patients (16.9%). Multivariate logistic modeling was used; independent risk factors for opioid refills included male sex, larger body mass index, and chronic preoperative opioid use, while meniscus resection demonstrated decreased likelihood of refills. Concomitant procedures, revision procedures, and presence of arthritis graded by the Outerbridge classification were not significant predictors of postoperative opioid refills. The naïve Bayesian model for extended postoperative opioid use demonstrated good fit with our cohort with an area under the curve of 0.79, sensitivity of 94.5%, positive predictive value (PPV) of 83%, and a detection rate of 78.2%. The two most important features in the model were preoperative opioid use and male sex.

CONCLUSION

After arthroscopic meniscus surgery, preoperative opioid consumption and male sex were the most significant predictors for sustained opioid use beyond 1 month postoperatively. Intraoperative arthritis was not an independent risk factor for continued refills. A machine learning algorithm performed with high accuracy, although with a high false positive rate, to function as a screening tool to identify patients filling additional narcotic prescriptions after surgery.

LEVEL OF EVIDENCE

III, retrospective comparative study.

Identifying Gait-Related Functional Outcomes in Post-Knee Surgery Patients Using Machine Learning: A Systematic Review

Modern lifestyles require new tools for determining a person's ability to return to daily activities after knee surgery. These quantitative instruments must feature high discrimination, be non-invasive, and be inexpensive. Machine learning is a revolutionary approach that has the potential to satisfy the aforementioned requirements and bridge the knowledge gap. The scope of this study is to summarize the results of a systematic literature review on the identification of gait-related changes and the determination of the functional recovery status of patients after knee surgery using advanced machine learning algorithms. The current systematic review was conducted using multiple databases in accordance with the PRISMA guidelines, including Scopus, PubMed, and Semantic Scholar. Six out of the 405 articles met our inclusion criteria and were directly related to the quantification of the recovery status using machine learning and gait data. The results were interpreted using appropriate metrics. The results demonstrated a recent increase in the use of sophisticated machine learning techniques that can provide robust decision-making support during personalized post-treatment interventions for knee-surgery patients.

A Machine Learning Framework for Assessing the Risk of Venous Thromboembolism in Patients Undergoing Hip or Knee Replacement

Venous thromboembolism (VTE) is a well-recognized complication that is prevalent in patients undergoing major orthopedic surgery (e.g., total hip arthroplasty and total knee arthroplasty). For years, to identify patients at high risk of developing VTE, physicians have relied on traditional risk scoring systems, which are too simplistic to capture the risk level accurately. In this paper, we propose a data-driven machine learning framework to identify such high-risk patients before they undergo a major hip or knee surgery. Using electronic health records of more than 392,000 patients who undergone a major orthopedic surgery, and following a guided feature selection using the genetic algorithm, we trained a fully connected deep neural network model to predict high-risk patients for developing VTE. We identified several risk factors for VTE that were not previously recognized. The best FCDNN model trained using the selected features yielded an area under the ROC curve (AUC) of 0.873, which was remarkably higher than the best AUC obtained by including only risk factors previously known in the medical literature. Our findings suggest several interesting and important insights. The traditional risk scoring tables that are being widely used by physicians to identify high-risk patients are not considering a comprehensive set of risk factors, nor are they as powerful as cutting-edge machine learning methods in distinguishing low- from high-risk patients.

Extending artificial intelligence research in the clinical domain: a theoretical perspective

Academic research to the utilization of artificial intelligence (AI) has been proliferated over the past few years. While AI and its subsets are continuously evolving in the fields of marketing, social media and finance, its application in the daily practice of clinical care is insufficiently explored. In this systematic review, we aim to landscape various application areas of clinical care in terms of the utilization of machine learning to improve patient care. Through designing a specific smart literature review approach, we give a new insight into existing literature identified with AI technologies in the clinical domain. Our review approach focuses on strategies, algorithms, applications, results, qualities, and implications using the Latent Dirichlet Allocation topic modeling. A total of 305 unique articles were reviewed, with 115 articles selected using Latent Dirichlet Allocation topic modeling, meeting our inclusion criteria. The primary result of this approach incorporates a proposition for future research direction, abilities, and influence of AI technologies and displays the areas of disease management in clinics. This research concludes with disease administrative ramifications, limitations, and directions for future research.

A novel application of neural networks to identify potentially effective combinations of biologic factors for enhancement of bone fusion/repair

INTRODUCTION

The use of biologic adjuvants (orthobiologics) is becoming commonplace in orthopaedic surgery. Among other applications, biologics are often added to enhance fusion rates in spinal surgery and to promote bone healing in complex fracture patterns. Generally, orthopaedic surgeons use only one biomolecular agent (ie allograft with embedded bone morphogenic protein-2) rather than several agents acting in concert. Bone fusion, however, is a highly multifactorial process and it likely could be more effectively enhanced using biologic factors in combination, acting synergistically. We used artificial neural networks, trained via machine learning on experimental data on orthobiologic interventions and their outcomes, to identify combinations of orthobiologic factors that potentially would be more effective than single agents. This use of machine learning applied to orthobiologic interventions is unprecedented.

METHODS

Available data on the outcomes associated with various orthopaedic biologic agents, electrical stimulation, and pulsed ultrasound were curated from the literature and assembled into a form suitable for machine learning. The best among many different types of neural networks was chosen for its ability to generalize over this dataset, and that network was used to make predictions concerning the expected efficacy of 2400 medically feasible combinations of 9 different agents and treatments.

RESULTS

The most effective combinations were high in the bone-morphogenic proteins (BMP) 2 and 7 (BMP2, 15mg; BMP7, 5mg), and in osteogenin (150ug). In some of the most effective combinations, electrical stimulation could substitute for osteogenin. Some other effective combinations also included bone marrow aspirate concentrate. BMP2 and BMP7 appear to have the strongest pairwise linkage of the factors analyzed in this study.

CONCLUSIONS

Artificial neural networks are powerful forms of artificial intelligence that can be applied readily in the orthopaedic domain, but neural network predictions improve along with the amount of data available to train them. This study provides a starting point from which networks trained on future, expanded datasets can be developed. Yet even this initial model makes specific predictions concerning potentially effective combinatorial therapeutics that should be verified experimentally. Furthermore, our analysis provides an avenue for further research into the basic science of bone healing by demonstrating agents that appear to be linked in function.

Artificial Intelligence in Modern Orthopaedics: Current and Future Applications

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With increasing computing power, artificial intelligence (AI) has gained traction in all aspects of health care delivery. Orthopaedics is no exception because the influence of AI technology has become intricately linked with its advancement as evidenced by increasing interest and research.

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This review is written for the orthopaedic surgeon to develop a better understanding of the main clinical applications and potential benefits of AI within their day-to-day practice.

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A brief and easy-to-understand foundation for what AI is and the different terminology used within the literature is first provided, followed by a summary of the newest research on AI applications demonstrating increased accuracy and convenience in risk stratification, clinical decision-making support, and robotically assisted surgery.