Abstract
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.
Keywords: Artificial intelligence, Machine learning, Shoulder, Deep learning, Elbow, Trends
Total health care spending in the United States increased from an estimated $1.4 trillion in 1996 ($5259 per person) to $3.1 trillion in 2016 ($9655 per person), and it has now reached $4.1 trillion in 2020.8,13 Such economic burden on patients and the system has driven an increased demand for high-quality, patient-centric, and cost-effective research and care. In order to improve various facets of the health care system, clinical applications of artificial intelligence (AI) and its subsets including machine learning (ML) and deep learning (DL) have been of interest. Several articles have highlighted and explained the principles of AI.3,14,27,28 Concisely, AI involves simulating human intelligence using technology. Algorithms can be trained and applied for many different purposes; a few examples include automating diagnosis of pathologies from magnetic resonance imaging images, predicting patient outcomes after surgery, and assisting in clinical examinations during telehealth visits.
Applications of AI in orthopedic surgery are being actively studied in many subspecialties, including hip, knee, and spine surgery. In the hip literature, Rouzrokh et al developed and tested AI models for automated measurements of acetabular component inclination and version on radiographs following total hip arthroplasty.35 In the knee literature, a recent study developed AI algorithms that performed well in predicting patients most likely to have a prolonged length of stay following revision total knee arthroplasty.20 In the spine literature, Rudisill et al developed and validated an AI model for predicting early-onset adjacent segment degeneration following anterior cervical discectomy and fusion using demographic, clinical, and radiographic variables.36
Likewise, several studies applying AI to shoulder surgery have been published.4,7,21,22,25,29,32 In one study, using a set of 21,544 elective total shoulder arthroplasty (TSA) cases, AI models were developed and performed well in predicting nonhome discharge and the occurrence of 1 or more postoperative complications following elective TSA.25 Similarly, AI models were developed and performed well in predicting patient satisfaction two years after primary anatomic and reverse TSA patients.32 These and other related investigations into the applications of AI in shoulder surgery have stirred much discussion. However, the trends on research in this area have not yet been quantified. Understanding the trends and details of publications in this area is helpful for characterizing academic interest in this area and for allowing the orthopedic community to identify journals and institutions of interest for research and training. Thus, the purpose of this study is to explore the general trends of research 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 ML OR DL OR neural networks). All titles, abstracts, and full-text articles were screened. Additional pertinent studies were also identified through evaluating the reference lists of the articles from the keyword search and additional search queries using keywords such as “rotator cuff” and others. Studies of all levels of evidence were included.
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, ML, or DL. Of the final articles selected, those in high-impact and/or relevant orthopedic journals underwent additional evaluation and characterization. These journals included Journal of Shoulder and Elbow Surgery (JSES), International, Journal of Shoulder and Elbow Arthroplasty, Orthopaedic Journal of Sports Medicine, American Journal of Sports Medicine, Arthroscopy, Journal of the American Academy of Orthopaedic Surgeons, Clinical Orthopaedics and Related Research), Acta Orthopaedica, The Journal of Bone & Joint Surgery, and Journal of Bone & Joint Surgery Open Access. Further analyses included country of first author, institutional affiliation of first author, and citation count. Citation count was determined using Scopus as consistent with previous studies.1,5,9,16 For most accurate trend analyses, only completed calendar years were used in analyses. A P value equal to or less than 0.05 was significant. All statistical and other analyses were performed using Microsoft Excel and SPSS Statistics (v28, 2021; IBM, Armonk, NY, USA).
Results
The primary search query using PubMed resulted in a total of 615 results. After identifying articles that met exclusion criteria, 79 articles remained. After evaluating reference lists and other relevant literature, 6 additional results were added for a total of 85. The annual number of articles increased from 1 article in 2006 to 24 articles in 2021 (Table I) (Fig. 1). The earliest publication included was in the year 2006. In the three most recently completed calendar years, 2021 had 24 publications, 2020 had 22 publications, and 2019 had 6 publications. There was a 4-fold increase in publications between 2019 and 2021, and a 6-fold increase between 2018 and 2021. The largest difference between any two consecutive years was between 2019 and 2020 with a difference of 16 publications. The average number of publications per year increased exponentially from 2010 to 2021 (R2 = 0.608; P = .003).
Table I.
Number of artificial intelligence publications in shoulder and elbow surgery by y.
| Y | Number of publications for all | Number of publications for select journals |
|---|---|---|
| 2022 | 20 | 3 |
| 2021 | 24 | 11 |
| 2020 | 22 | 6 |
| 2019 | 6 | 2 |
| 2018 | 4 | 2 |
| 2017 | 1 | 0 |
| 2016 | 4 | 0 |
| 2015 | 1 | 0 |
| 2014 | 1 | 0 |
| 2013 | 0 | 0 |
| 2012 | 1 | 0 |
| 2011 | 0 | 0 |
| 2010 | 0 | 0 |
| 2006 | 1 | 0 |
Figure 1.
Annual number of publications in shoulder and elbow surgery.
When analyzing only the high-impact and/or relevant orthopedic journals, there were 24 total publications (Table II). Of the 24 publications, 16 focused on shoulder arthroplasty, 0 focused on elbow arthroplasty, 1 focused on proximal humerus fractures, 1 focused on anterior shoulder instability, and 6 were miscellaneous. The earliest year was 2018 with 2 publications and the last completed calendar year was 2021 with 11 publications (Table I). The total publications per year increased more than 5-fold from 2 in 2018 to 11 in 2021. The three journals with the most publications were JSES (12), followed by Arthroscopy (2), and Clinical Orthopaedics and Related Research (2) (Table II). The most common country for the first author’s affiliation was the United States of America (20). The two most common first author institution affiliations were Atrium Health Wake Forest Baptist Health/Wake Forest School of Medicine (4) and Cleveland Clinic (2). The most cited article (127 times) was published by Chung et al and titled “Automated detection and classification of the proximal humerus fracture by using deep learning algorithm” in Acta Orthopaedica.6 The next two most cited articles were by Gowd et al in JSES (18 times) and Biron et al in Journal of the American Academy of Orthopaedic Surgeons (18 times).4,11
Table II.
Number of artificial intelligence publications in shoulder and elbow by journal.
| Journal | Number of publications |
|---|---|
| JSES | 12 |
| JSES International | 1 |
| JSEA | 1 |
| Orthopaedic Journal of Sports Medicine | 1 |
| American Journal of Sports Medicine | 1 |
| Arthroscopy | 2 |
| Journal of the American Academy of Orthopaedic Surgeons | 1 |
| Clinical Orthopaedics and Related Research | 2 |
| Acta Orthopaedica | 1 |
| JBJS | 1 |
| JBJS Open Access | 1 |
JSES, Journal of Shoulder and Elbow Surgery; JSEA, Journal of Shoulder and Elbow Arthroplasty; JBJS, Journal of Bone and Joint Surgery.
Discussion
AI and its subsets are being investigated for their applications and utility in orthopedic surgery, including total joint arthroplasty, spine surgery, and shoulder and elbow surgery. In shoulder and elbow surgery, advancements are being made in applying AI for predicting patient complications, predicting functional outcomes, assisting in evaluating rotator cuff tears from imaging, and more. However, no study has yet provided quantitative data on the trends and characteristics of publications on applying AI to shoulder and elbow surgery. 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 2010, with the most rapid growth beginning between the years of 2019 and 2020.
The largest interval growth in publications happened from 6 publications in 2019 to 22 publications in 2020. Additionally, despite the literature search including less than half of the 2022 year (January 1-May 12), the number of publications found in 2022 was almost already equivalent to that of the completed year of 2021. Together, this data shows that the spark in interest and exponential growth in AI studies for shoulder and elbow surgery began recently in the past few years. The reason for this is likely multifactorial. First, with rising health care costs and disparities in care, much attention has been brought to the importance of delivering high-quality, low-cost, and patient-centric care. With increasing promise of AI tools being able to assist in lowering costs and improving quality of care, researchers, physicians, and patients are becoming more aware of and interested in AI.2,10,12,15,23,24,26,31 Second, the optimism and success seen in AI research and applications in other orthopedic subspecialties such as hip and knee arthroplasty and spine surgery has likely spurred more interest in shoulder and elbow AI research.33,34,39 For example, Karnuta et al trained and externally validated a DL model to classify one of eight femoral-sided total hip arthroplasty implants directly from anteroposterior plain radiographs, which performed with a mean speed of 0.02 seconds per image, area under the curve (AUC) of 0.99, and accuracy of nearly 98%.17 Third, although AI and its subsets have been around for several decades, technological advances such as faster computer processing speeds have allowed research in this area with big data to be more efficient and effective.18,19,38
To better understand which areas of AI applications are of most academic interest in shoulder and elbow surgery, the citation count for articles published in high-impact and/or relevant orthopedic journals was examined. The most cited article involved applying AI to assist with detecting and classifying proximal humerus fractures from plain anteroposterior shoulder radiographs. In this study by Chung et al, a deep convolutional neural network was developed and tested using 1891 images. The CNN performed excellently in distinguishing normal shoulders from proximal humerus fractures (0.996 AUC) and classifying fracture type (AUC 0.90-0.98). Importantly, when compared to general physicians, general orthopedists, or orthopedic shoulder specialists, the CNN often performed superiorly or similarly. As supported by the high citation count, this shows the great interest that orthopedic surgeons and other musculoskeletal investigators have in AI applications that assist in detecting and classifying shoulder and elbow pathology directly from images.
Similarly, the next two highest cited articles involved predicting patient complications and length of stay following TSA.4,11 Gowd et al used a national database to train and validate several ML algorithms for predicting any adverse event, extended length of stay, surgical site infection, and more.11 Biron et al also used a national database and developed a random forest model that performed well (AUC 0.77) in predicting patients with a short length of stay (1 day or less) following TSA.4 The high citation count for both previously described articles shows the interest of orthopedic investigators in using AI for risk-stratification and prediction analyses.
This study is not without limitations. Only PubMed was used in this study, so it is possible that not all articles pertaining to AI applications in shoulder and elbow surgery were included. However, PubMed is one of the most used and robust biomedical search engines providing access to over 34 million, high-quality research citations and abstracts that are relevant to physicians.30 Additionally, the citation counts from Scopus may not include all citations, as the counts are limited to only those journals index within Scopus. However, Scopus’ coverage is broad as it contains over 25,000 peer-reviewed journals and has more than 83 million records.37
Conclusion
This study provided quantitative evidence for the first time that research and publications in applying AI to shoulder and elbow surgery are growing exponentially. Many different areas of application are being explored, including automated evaluation and classification of rotator cuff tears from images, identification of implants, predicting post complications and functional outcomes following shoulder arthroplasty. As this study found an exponential growth in annual publications since 2010, it is likely that this area will continue to grow expansively for many years to come. Orthopedic surgeons should be aware of these trends as advancements in AI applications in shoulder and elbow surgery have the potential to change daily clinical and operative practice.
Disclaimers
Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Conflicts of interest: The authors, their immediate families, and any research foundation with which they are affiliated have not received any financial payments or other benefits from any commercial entity related to the subject of this article.
Footnotes
Ethical approval for this study was waived by the institutional review board.
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