Artificial intelligence in arthroplasty

Artificial intelligence technology and ultrasound-guided nerve block for analgesia in total knee arthroplasty

BACKGROUND

Knee diseases are more common in middle-aged and elderly people, so artificial knee replacement is also more used in middle-aged and elderly people. Although the patient's pain can be reduced through surgery, often accompanied by moderate pain after surgery and neutralization, which not only increases the psychological burden of the patient, but also greatly reduces the postoperative recovery effect, and may also lead to the occurrence of postoperative adverse events in severe cases.

AIM

To investigate the analgesic effect of artificial intelligence (AI) and ultrasound-guided nerve block in total knee arthroplasty (TKA).

METHODS

A total of 92 patients with TKA admitted to our hospital from January 2021 to January 2022 were opted and divided into two groups according to the treatment regimen. The control group received combined spinal-epidural anesthesia. The research group received AI technique combined with ultrasound-guided nerve block anesthesia. The sensory block time, motor block time, visual analogue scale (VAS) at different time points and complications were contrasted between the two groups.

RESULTS

The time of sensory block onset and sensory block perfection in the research group was shorter than those in the control group, but the results had no significant difference (P > 0.05). Duration of sensory block in the research group was significantly longer than those in the control group (P < 0.05). The time of motor block onset and motor block perfection in the research group was shorter than those in the control group, but the results had no significant difference (P > 0.05). Duration of motor block in the research group was significantly longer than those in the control group. The VAS scales of the research group were significantly lower than that of the control group at different time points (P < 0.05). The postoperative hip flexion and abduction range of motion in the research group were significantly better than those in the control group at different time points (P < 0.05). The incidence of complications was significantly lower in the research group than in the control group (P = 0.049).

CONCLUSION

In TKA, the combination of AI technology and ultrasound-guided nerve block has a significantly effect, with fewer postoperative complications and significantly analgesic effect, which is worthy of application.

Validation of Machine Learning Model Performance in Predicting Blood Transfusion After Primary and Revision Total Hip Arthroplasty

BACKGROUND

The rates of blood transfusion following primary and revision total hip arthroplasty (THA) remain as high as 9% and 18%, respectively, contributing to patient morbidity and healthcare costs. Existing predictive tools are limited to specific populations, thereby diminishing their clinical applicability. This study aimed to externally validate our previous institutionally developed machine learning (ML) algorithms to predict the risk of postoperative blood transfusion following primary and revision THA using national inpatient data.

METHODS

Five ML algorithms were trained and validated using data from 101,266 primary THA and 8,594 revision THA patients from a large national database to predict postoperative transfusion risk after primary and revision THA. Models were assessed and compared based on discrimination, calibration, and decision curve analysis.

RESULTS

The most important predictors of transfusion following primary and revision THA were preoperative hematocrit (<39.4%) and operation time (>157 minutes), respectively. All ML models demonstrated excellent discrimination (area under the curve (AUC) >0.8) in primary and revision THA patients, with artificial neural network (AUC = 0.84, slope = 1.11, intercept = -0.04, Brier score = 0.04), and elastic-net-penalized logistic regression (AUC = 0.85, slope = 1.08, intercept = -0.01, and Brier score = 0.12) performing best, respectively. On decision curve analysis, all 5 models demonstrated a higher net benefit than the conventional strategy of intervening for all or no patients in both patient cohorts.

CONCLUSIONS

This study successfully validated our previous institutionally developed ML algorithms for the prediction of blood transfusion following primary and revision THA. Our findings highlight the potential generalizability of predictive ML tools developed using nationally representative data in THA patients.

Technological innovations in shoulder replacement: current concepts and the future of robotics in total shoulder arthroplasty

BACKGROUND

Total shoulder arthroplasty (TSA) has been rapidly evolving over the last several decades, with innovative technological strategies being investigated and developed in order to achieve optimal component precision and joint alignment and stability, preserve implant longevity, and improve patient outcomes. Future advancements such as robotic-assisted surgeries, augmented reality, artificial intelligence, patient-specific instrumentation (PSI) and other peri- and preoperative planning tools will continue to revolutionize TSA. Robotic-assisted arthroplasty is a novel and increasingly popular alternative to the conventional arthroplasty procedure in the hip and knee but has not yet been investigated in the shoulder. Therefore, the purpose of this study was to conduct a narrative review of the literature on the evolution and projected trends of technological advances and robotic assistance in total shoulder arthroplasty.

METHODS

A narrative synthesis method was employed for this review, rather than a meta-analysis or systematic review of the literature. This decision was based on 2 primary factors: (1) the lack of eligible, peer-reviewed studies with high-quality level of evidence available for review on robotic-assisted shoulder arthroplasty, and (2) a narrative review allows for a broader scope of content analysis, including a comprehensive review of all technological advances-including robotics-within the field of TSA. A general literature search was performed using PubMed, Embase, and Cochrane Library databases. These databases were queried by 2 independent reviewers from database inception through November 11, 2022, for all articles investigating the role of robotics and technology assistance in total shoulder arthroplasty. Inclusion criteria included studies describing "shoulder arthroplasty" and "robotics."

RESULTS

After exclusion criteria were applied, 4 studies on robotic-assisted TSA were described in the review. Given the novelty of this technology and limited data on robotics in TSA, these studies consisted of a literature review, nonvalidated experimental biomechanical studies in sawbones models, and preclinical proof-of-concept cadaveric studies using prototype robotic technology primarily in conjunction with PSI. The remaining studies described the technological advancements in TSA, including PSI, computer-assisted navigation, artificial intelligence, machine learning, and virtual, augmented, and mixed reality. Although not yet commercially available, robotic-assisted TSA confers the theoretical advantages of precise humeral head cuts for restoration of proximal humerus anatomy, more accurate glenoid preparation, and improved soft-tissue assessment in limited early studies.

CONCLUSION

The evidence for the use of robotics in total hip arthroplasty and total knee arthroplasty demonstrates improved component accuracy, more precise radiographic measurements, and improved early/mid-term patient-reported and functional outcomes. Although no such data currently exist for shoulder arthroplasty given that the technology has not yet been commercialized, the lessons learned from robotic hip and knee surgery in conjunction with its rapid adoption suggests robotic-assisted TSA is on the horizon of innovation. By achieving a better understanding of the past, present, and future innovations in TSA through this narrative review, orthopedic surgeons can be better prepared for future applications.

Artificial intelligence in orthopaedic surgery

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

Application of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty: a systematic review

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

Recent Trends and Hotspots in Hip Arthroplasty: A Bibliometric Analysis and Visualization Study of Last Five-Year Publications

Objectives

Bibliometric analysis is one of the most prevalent methods for analyzing and predicting research trends of particular subjects. Through a bibliometric analysis, the present study sought to look into and depict the hotspots and research trends in hip arthroplasty research over the previous five years.

Methods

The Web of Science Core Collection database was used to find studies on hip arthroplasty published between 2018 and 2022. The VOS viewer, Cite Space, and Bibliometrix were used to carry out the bibliometric study and network visualization.

Results

During the last five years, 5,708 hip arthroplasty publications were cited 40,765 times. The United States and the Journal of Arthroplasty were the top countries and journals regarding the number of studies, respectively. The top 10 global high-impact documents were determined using the citation ranking and citation burst. The most frequently referenced research revealed the epidemiological aspects of hip arthroplasty, perioperative care after hip arthroplasty, COVID-19, periprosthetic joint infections, opioid medicines, stability, and osteonecrosis were the hot topics in hip arthroplasty research. Keyword burst analysis showed that the research trends in hip arthroplasty through 2022 were patient-reported outcome measures (PROM), depression, racial disparity, and artificial intelligence (AI). The analysis of the subject areas revealed the close connections and relationships between different subject areas, as demonstrated by the figures.

Conclusion

The hip arthroplasty research community is very productive and highly centralized. Periprosthetic joint infection, dual-mobility cups, spinopelvic mobility, direct anterior approach, outpatient total hip arthroplasty, polyethylene, periprosthetic fracture, acetabular defects, tranexamic acid, developmental dysplasia of the hip, and safety-net hospitals were recent trends in hip arthroplasty research. Patient-reported outcome measures, depression, racial disparities, and AI were research hotspots in hip arthroplasty.

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.

Research on artificial intelligence in shoulder and elbow surgery is increasing

Background

Total health care spending in the United States is increasing. In order to improve our delivery of high-quality, patient-centric, and cost-effective care, artificial intelligence (AI) and its subsets are being increasingly explored and utilized in medicine. Applications of AI in orthopedic surgery, including shoulder and elbow surgery, are being actively studied and have stirred much discussion. However, the trends of research on AI applications in shoulder and elbow surgery have not yet been quantified. Thus, the purpose of this study is to explore the general trends of research in applying AI to shoulder and elbow surgery and to examine characteristics of these research publications.

Methods

A literature search was conducted using PubMed for all articles published between January 1, 2000 and May 12, 2022. The primary search query used was as follows: (shoulder) and (AI OR machine learning OR deep learning OR neural networks). Exclusion criteria were as follows: (1) not pertinent to orthopedic surgeons (2) not pertaining to shoulder or elbow surgery, and (3) not pertaining to AI, machine learning, and deep learning. Selected articles in high-impact and relevant orthopedic journals were further characterized and analyzed.

Results

The annual number of articles increased from 1 article in 2006 to 24 articles in 2021. There was a 4-fold increase in publications between 2019 and 2021, and a 6-fold increase between 2018 and 2021. The average number of publications per year increased exponentially from 2010 to 2021 (R2 = 0.608; P = .003). The three journals with the most publications were Journal of Shoulder and Elbow Surgery (12), followed by Arthroscopy (2), and Clinical Orthopaedics and Related Research (2).

Conclusion

This study provides quantitative evidence for the first time that publications applying AI and its subsets to shoulder and elbow surgery are growing exponentially since the year 2010, with the most rapid growth beginning between the years of 2019 and 2020.