Applications of artificial intelligence and machine learning for the hip and knee surgeon: current state and implications for the future

The Role of Artificial Intelligence and Emerging Technologies in Advancing Total Hip Arthroplasty

Total hip arthroplasty (THA) is a widely performed surgical procedure that has evolved significantly due to advancements in artificial intelligence (AI) and robotics. As demand for THA grows, reliable tools are essential to enhance diagnosis, preoperative planning, surgical precision, and postoperative rehabilitation. AI applications in orthopedic surgery offer innovative solutions, including automated hip osteoarthritis (OA) diagnosis, precise implant positioning, and personalized risk stratification, thereby improving patient outcomes. Deep learning models have transformed OA severity grading and implant identification by automating traditionally manual processes with high accuracy. Additionally, AI-powered systems optimize preoperative planning by predicting the hip joint center and identifying complications using multimodal data. Robotic-assisted THA enhances surgical precision with real-time feedback, reducing complications such as dislocations and leg length discrepancies while accelerating recovery. Despite these advancements, barriers such as cost, accessibility, and the steep learning curve for surgeons hinder widespread adoption. Postoperative rehabilitation benefits from technologies like virtual and augmented reality and telemedicine, which enhance patient engagement and adherence. However, limitations, particularly among elderly populations with lower adaptability to technology, underscore the need for user-friendly platforms. To ensure comprehensiveness, a structured literature search was conducted using PubMed, Scopus, and Web of Science. Keywords included "artificial intelligence", "machine learning", "robotics", and "total hip arthroplasty". Inclusion criteria emphasized peer-reviewed studies published in English within the last decade focusing on technological advancements and clinical outcomes. This review evaluates AI and robotics' role in THA, highlighting opportunities and challenges and emphasizing further research and real-world validation to integrate these technologies into clinical practice effectively.

XGBoost-SHAP-based interpretable diagnostic framework for knee osteoarthritis: a population-based retrospective cohort study

OBJECTIVE

To use routine demographic and clinical data to develop an interpretable individual-level machine learning (ML) model to diagnose knee osteoarthritis (KOA) and to identify highly ranked features.

METHODS

In this retrospective, population-based cohort study, anonymized questionnaire data was retrieved from the Wu Chuan KOA Study, Inner Mongolia, China. After feature selections, participants were divided in a 7:3 ratio into training and test sets. Class balancing was applied to the training set for data augmentation. Four ML classifiers were compared by cross-validation within the training set and their performance was further analyzed with an unseen test set. Classifications were evaluated using sensitivity, specificity, positive predictive value, negative predictive value, accuracy, area under the curve(AUC), G-means, and F1 scores. The best model was explained using Shapley values to extract highly ranked features.

RESULTS

A total of 1188 participants were investigated in this study, among whom 26.3% were diagnosed with KOA. Comparatively, XGBoost with Boruta exhibited the highest classification performance among the four models, with an AUC of 0.758, G-means of 0.800, and F1 scores of 0.703. The SHAP method reveals the top 17 features of KOA according to the importance ranking, and the average of the experience of joint pain was recognized as the most important features.

CONCLUSIONS

Our study highlights the usefulness of machine learning in unveiling important factors that influence the diagnosis of KOA to guide new prevention strategies. Further work is needed to validate this approach.

Clinical validation of a deep learning-based approach for preoperative decision-making in implant size for total knee arthroplasty

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Precision of Cup Positioning Using a Novel Computed Tomography Based Navigation System in Total Hip Arthroplasty

Background and Objectives: Navigation systems are designed to enhance surgical precision, improving patient outcomes and reducing the risk of implant misplacement. In this study, we have evaluated a novel orthopedic surgical platform that utilizes CT imaging with AI-based algorithms to automate several critical aspects of total hip arthroplasty. It contains three modules-preoperative planning, navigation during surgery, and follow-up analysis. The primary objective of the current study was to evaluate the precision of the navigation tool in cup placement, i.e., whether the information displayed for navigation correctly reflected the actual position of the implant. Materials and Methods: Surgery outcomes of 15 inter-rater measurements on human cadavers and 18 surgeries on patients who underwent total hip replacement using the navigation tool were analyzed. Results: In the inter-rater assessment, the mean errors were -0.31 ± 1.42° for anteversion, 1.06 ± 1.73° for inclination, and -0.94 ± 1.76 mm for cup position depth. In patients' surgeries, the mean errors were -0.07 ± 2.72° for anteversion, -0.2 ± 0.86° for inclination, and 0.28 ± 0.78 mm for cup depth. Conclusions: The navigation tool offers intra-operative guidance on notable precision in cup placement, thereby effectively mitigating the risk of cup malpositioning outside the patient-specific safe zone.

Development of a diagnostic support system for distal humerus fracture using artificial intelligence

PURPOSE

AI has shown promise in automating and improving various tasks, including medical image analysis. Distal humerus fractures are a critical clinical concern that requires early diagnosis and treatment to avoid complications. The standard diagnostic method involves X-ray imaging, but subtle fractures can be missed, leading to delayed or incorrect diagnoses. Deep learning, a subset of artificial intelligence, has demonstrated the ability to automate medical image analysis tasks, potentially improving fracture identification accuracy and reducing the need for additional and cost-intensive imaging modalities (Schwarz et al. 2023). This study aims to develop a deep learning-based diagnostic support system for distal humerus fractures using conventional X-ray images. The primary objective of this study is to determine whether deep learning can provide reliable image-based fracture detection recommendations for distal humerus fractures.

METHODS

Between March 2017 and March 2022, our tertiary hospital's PACS data were evaluated for conventional radiography images of the anteroposterior (AP) and lateral elbow for suspected traumatic distal humerus fractures. The data set consisted of 4931 images of patients seven years and older, after excluding paediatric images below seven years due to the absence of ossification centres. Two senior orthopaedic surgeons with 12 + years of experience reviewed and labelled the images as fractured or normal. The data set was split into training sets (79.88%) and validation tests (20.1%). Image pre-processing was performed by cropping the images to 224 × 224 pixels around the capitellum, and the deep learning algorithm architecture used was ResNet18.

RESULTS

The deep learning model demonstrated an accuracy of 69.14% in the validation test set, with a specificity of 95.89% and a positive predictive value (PPV) of 99.47%. However, the sensitivity was 61.49%, indicating that the model had a relatively high false negative rate. ROC analysis showed an AUC of 0.787 when deep learning AI was the reference and an AUC of 0.580 when the most senior orthopaedic surgeon was the reference. The performance of the model was compared with that of other orthopaedic surgeons of varying experience levels, showing varying levels of diagnostic precision.

CONCLUSION

The developed deep learning-based diagnostic support system shows potential for accurately diagnosing distal humerus fractures using AP and lateral elbow radiographs. The model's specificity and PPV indicate its ability to mark out occult lesions and has a high false positive rate. Further research and validation are necessary to improve the sensitivity and diagnostic accuracy of the model for practical clinical implementation.

Ortho-digital dynamics: Exploration of advancing digital health technologies in musculoskeletal disease management

Background

Musculoskeletal (MSK) disorders, affecting billions of people worldwide, pose significant challenges to the healthcare system and require effective management models. The rapid development of digital healthcare technologies (DHTs) has revolutionized the healthcare industry. DHT-based interventions have shown promising clinical benefits in managing MSK disorders, alleviating pain, and improving functional impairment. There is, however, no bibliometric analysis of the overall trends on this topic.

Methods

We extracted all relevant publications from the Web of Science Core Collection (WoSCC) database until April 30, 2023. We performed bibliometric analysis and visualization using CiteSpace, VOSviewer, and R software. Annual trends of publications, countries/regions distributions, funding agencies, institutions, co-cited journals, author contributions, references, core journals, and keywords and research hotspots were analyzed.

Results

A total of 6810 papers were enrolled in this study. Publications have increased drastically from 16 in 1995 to 1198 in 2022, with 4067 articles published in the last five years. In all, 53 countries contributed with publications to this research area. The United States, the United Kingdom, and China were the most productive countries. Harvard University was the most contributing institution. Regarding keywords, research focuses include artificial intelligence, deep learning, machine learning, telemedicine, rehabilitation, and robotics.

Conclusion

The COVID-19 pandemic has further accelerated the adoption of DHTs, highlighting the need for remote care options. The analysis reveals the positive impact of DHTs on improving physician productivity, enhancing patient care and quality of life, reducing healthcare expenditures, and predicting outcomes. DHTs are a hot topic of research not only in the clinical field but also in the multidisciplinary intersection of rehabilitation, nursing, education, social and economic fields. The analysis identifies four promising hotspots in the integration of DHTs in MSK pain management, biomechanics assessment, MSK diagnosis and prediction, and robotics and tele-rehabilitation in arthroplasty care.

A role for artificial intelligence applications inside and outside of the operating theatre: a review of contemporary use associated with total knee arthroplasty

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Digital Orthopedics: The Future Developments of Orthopedic Surgery

Digital medicine is a new type of medical treatment that applies modern digital information technologies to entire medical procedures [...].

Deconstructing forearm casting task by videos with step-by-step simulation teaching improved performance of medical students: is making working student's memory work better similar to a process of artificial intelligence or just an improvement of the prefrontal cortex homunculus?

PURPOSE

To compare two teaching methods of a forearm cast in medical students through simulation, the traditional method (Trad) based on a continuous demonstration of the procedure and the task deconstruction method (Decon) with the procedure fragmenting into its constituent parts using videos.

METHODS

During simulation training of the below elbow casting technique, 64 medical students were randomized in two groups. Trad group demonstrated the entire procedure without pausing. Decon group received step-wise teaching with educational videos emphasizing key components of the procedure. Direct and video evaluations were performed immediately after training (day 0) and at six months. Performance in casting was assessed using a 25-item checklist, a seven item global rating scale (GRS Performance), and a one item GRS (GRS Final Product).

RESULTS

Fifty-two students (Trad n = 24; Decon n = 28) underwent both day zero and six month assessments. At day zero, the Decon group showed higher performance via video evaluation for OSATS (p = 0.035); GRS performance (p < 0.001); GRS final product (p < 0.001), and for GRS performance (p < 0.001) and GRS final product (p = 0.011) via direct evaluation. After six months, performance was decreased in both groups with ultimately no difference in performance between groups via both direct and video evaluation. Having done a rotation in orthopaedic surgery was the only independent factor associated to higher performance.

CONCLUSIONS

The modified video-based version simulation led to a higher performance than the traditional method immediately after the course and could be the preferred method for teaching complex skills.

Artificial intelligence and machine learning as a viable solution for hip implant failure diagnosis-Review of literature and in vitro case study

The digital health industry is experiencing fast-paced research which can provide digital care programs and technologies to enhance the competence of healthcare delivery. Orthopedic literature also confirms the applicability of artificial intelligence (AI) and machine learning (ML) models to medical diagnosis and clinical decision-making. However, implant monitoring after primary surgery often happens with a wellness visit or when a patient complains about it. Neglecting implant design and other technical errors in this scenario, unmonitored circumstances, and lack of post-surgery monitoring may ultimately lead to the implant system's failure and leave us with the only option of high-risk revision surgery. Preventive maintenance seems to be a good choice to identify the onset of an irreversible prosthesis failure. Considering all these aspects for hip implant monitoring, this paper explores existing studies linking ML models and intelligent systems for hip implant diagnosis. This paper explores the feasibility of an alternative continuous monitoring technique for post-surgery implant monitoring backed by an in vitro ML case study. Tribocorrosion and acoustic emission (AE) data are considered based on their efficacy in determining irreversible alteration of implant material to prevent total failures. This study also facilitates the relevance of developing an artificially intelligent implant monitoring methodology that can function with daily patient activities and how it can influence the digital orthopedic diagnosis. AI-based non-invasive hip implant monitoring system enabling point-of-care testing.

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.

Automatic assessment of knee osteoarthritis severity in portable devices based on deep learning

BACKGROUND

For knee osteoarthritis, the commonly used radiology severity criteria Kellgren-Lawrence lead to variability among surgeons. Most existing diagnosis models require preprocessed radiographs and specific equipment.

METHODS

All enrolled patients diagnosed with KOA who met the criteria were obtained from **** Hospital. This study included 2579 images shot from posterior-anterior X-rays of 2,378 patients. We used RefineDet to train and validate this deep learning-based diagnostic model. After developing the model, 823 images of 697 patients were enrolled as the test set. The whole test set was assessed by up to 5 surgeons and this diagnostic model. To evaluate the model's performance we compared the results of the model with the KOA severity diagnoses of surgeons based on K-L scales.

RESULTS

Compared to the diagnoses of surgeons, the model achieved an overall accuracy of 0.977. Its sensitivity (recall) for K-L 0 to 4 was 1.0, 0.972, 0.979, 0.983 and 0.989, respectively; for these diagnoses, the specificity of this model was 0.992, 0.997, 0.994, 0.991 and 0.995. The precision and F1-score were 0.5 and 0.667 for K-L 0, 0.914 and 0.930 for K-L 1, 0.978 and 0.971 for K-L 2, 0.981 and 0.974 for K-L 3, and 0.988 and 0.985 for K-L 4, respectively. All K-L scales perform AUC > 0.90. The quadratic weighted Kappa coefficient between the diagnostic model and surgeons was 0.815 (P < 0.01, 95% CI 0.727-0.903). The performance of the model is comparable to the clinical diagnosis of KOA. This model improved the efficiency and avoided cumbersome image preprocessing.

CONCLUSION

The deep learning-based diagnostic model can be used to assess the severity of KOA in portable devices according to the Kellgren-Lawrence scale. On the premise of improving diagnostic efficiency, the results are highly reliable and reproducible.

History of bone acoustic in fracture diagnosis: crepitus in antiquity; bone percussion with Auenbrugger; bone auscultation with Laennec and Lisfranc; monitoring cementless hip arthroplasty fixation with acoustic and sensor

PURPOSE

The problems posed by trauma, fractures, and dislocations have not changed in human history. The traumas of prehistoric persons were similar to those observed by Imhotep, Hippocrates, and Galen or, more recently, by Ambroise Paré, Watson Jones, and Böhler. And the current road traumas are probably no more severe than those caused by mammoths, the construction of the pyramids, or middle age wars. Diagnostic methods have evolved, and the advent of radiography has revolutionized the diagnosis of traumatology. Before discovering radiography, another physical phenomenon made it possible to help in the diagnosis of fractures. This physical phenomenon is acoustic.

METHODS

Curiously, no history of acoustics in fracture diagnosis has been published so far. This article proposes briefly reviewing the history and evolution of acoustics in orthopaedic surgery from antiquity to the present day.

RESULTS

Before the invention of radiography by Conrad Roentgen in 1895, the surgeons described crepitus as the most critical sign of fractures in antiquity. Surgeons remarked during the eighteenth and nineteenth century that bone was a good sound-conductor. Physicians improved first the diagnosis of fractures by using percussion established by Auenbrugger in 1755. The principle of chest mediate auscultation with a stethoscope was described by Laennec in 1818. Lisfranc used the stethoscope to amplify the crepitus sound of fractures. Surgeons also developed association of percussion and auscultation with a stethoscope to diagnose and reduce fracture. Recently, acoustic emission technology has seen a recent increase in applications to prevent femur fractures during cementless fixation.

CONCLUSION

The acoustic properties of bones were known to a prehistoric person who knew how to make flutes from animal or human bones. Surgeons used them for the diagnosis of fractures before radiography. Acoustic properties of bones currently remain a subject of research for the prevention of fractures.