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RESEARCH ARTICLE
Artificial intelligence in orthopaedics: A
scoping review
Simon J. FedererID
¤
*
☯
, Gareth G. Jones
☯
MSk Lab, Sir Michael Uren Hub, Imperial College London, London, United Kingdom
☯These authors contributed equally to this work.
¤Current address: Department of Trauma & Orthopaedics, St Richard’s Hospital, Chichester, United
Kingdom
*simon.federer@nhs.net
Abstract
There is a growing interest in the application of artificial intelligence (AI) to orthopaedic sur-
gery. This review aims to identify and characterise research in this field, in order to under-
stand the extent, range and nature of this work, and act as springboard to stimulate future
studies. A scoping review, a form of structured evidence synthesis, was conducted to sum-
marise the use of AI in orthopaedics. A literature search (1946–2019) identified 222 studies
eligible for inclusion. These studies were predominantly small and retrospective. There has
been significant growth in the number of papers published in the last three years, mainly
from the USA (37%). The majority of research used AI for image interpretation (45%) or as a
clinical decision tool (25%). Spine (43%), knee (23%) and hip (14%) were the regions of the
body most commonly studied. The application of artificial intelligence to orthopaedics is
growing. However, the scope of its use so far remains limited, both in terms of its possible
clinical applications, and the sub-specialty areas of the body which have been studied. A
standardized method of reporting AI studies would allow direct assessment and compari-
son. Prospective studies are required to validate AI tools for clinical use.
Introduction
Interest in the application of artificial intelligence (AI) in healthcare has surged in recent years
[1]. Computer systems are increasingly able to perform tasks that normally require human
intelligence, facilitated by improvements in data storage and computer processing. Despite the
interest, incorporation of AI into clinical practice is in its infancy [2]. AI tools are currently in
use, for example; in segmentation of three-dimensional optical coherence tomography scans
to aid referrals in ophthalmology [3], and detection of atrial fibrillation by a smartphone algo-
rithm and a single lead electrocardiography device in primary care [4]. The increase in digital
medical imaging and information collected in databases and orthopaedic registries, provide
large datasets ideal for the development of AI algorithms. These have the potential to improve
patient care at a number of levels including; diagnosis, management, research and systems
analysis [5].
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OPEN ACCESS
Citation: Federer SJ, Jones GG (2021) Artificial
intelligence in orthopaedics: A scoping review.
PLoS ONE 16(11): e0260471. https://doi.org/
10.1371/journal.pone.0260471
Editor: Thippa Reddy Gadekallu, Vellore Institute of
Technology: VIT University, INDIA
Received: May 19, 2021
Accepted: November 11, 2021
Published: November 23, 2021
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
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https://doi.org/10.1371/journal.pone.0260471
Copyright: ©2021 Federer, Jones. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: The dataset may be
found at this URL: https://data.mendeley.com/
datasets/xvkr6t263v/1.
Funding: The author(s) received no specific
funding for this work.
The volume and variety of data collected from individuals has facilitated the advancement of AI
across multiple industries. Concerns regarding how personal data is stored and utilised prompted
legislation to protect this information. The General Data Protection Regulation (GDPR) was intro-
duced in the European Union (EU) in 2018, and some medical registries have struggled to gather
data in the same volume since. However, registries where patient consent has been a priority, such
as the National Joint Registry (NJR) in the UK, have not seen a sharp decrease. The NJR holds
information on over 3 million arthroplasty procedures since 2003 [6]. Orthopaedic registries are
some of the largest in healthcare and are primed for the application of AI.
Artificial intelligence remains a relatively new field for most orthopaedic surgeons, and
understanding the extent, range and nature of work conducted so far is useful as a springboard
to identify potential new applications and areas for research. With this goal in mind, we con-
ducted a scoping review, which is a form of structured evidence synthesis suited to this task.
The aims were to: 1) identify the number of research studies using AI in orthopaedics and 2)
summarize how and where these studies have applied AI to the field of orthopaedics.
Methods
A scoping review was chosen due to the breadth of the research topic and the expected varia-
tion in study design, and was conducted using the Arksey and O’Malley framework [7]. The
PRISMA-ScR checklist was utilised to ensure completeness (S1 Table) [8].
Literature search and eligible studies
A literature search of studies in English was conducted (1946–2019) using Ovid (Embase &
Medline) and Scopus. The search timeframe was chosen to ensure early studies were not
missed. The literature search was performed on 30/8/19. The search strategy is shown in Fig 1.
The search terms used are shown in S2 and S3 Tables.
The review focused on summarising the use of AI in applications relevant to clinical prac-
tice rather than related basic science. Hence, the following inclusion criteria were used: (a)
studies which directly applied artificial intelligence to orthopaedic clinical practice or b) the
outcomes of the study had the potential to be directly applied to orthopaedic clinical practice.
Abstracts, conference proceedings, articles not in English and review, commentary or edi-
torial articles were not eligible for inclusion. Articles relating to the following were also
excluded: cancer/oncology, biomechanics, gait analysis without clinical application, image seg-
mentation alone without a direct clinical application, basic science, neuromuscular disorders,
rehabilitation, prosthetics, natural language processing of radiology reports and wearable sen-
sors. These articles were excluded to ensure the review maintained a clinical focus and was
applicable to a general orthopaedic audience.
The literature search was performed by one investigator (SF). Abstract screening and full
text reviews were performed independently by two investigators (SF and GJ). There was full
agreement on the studies selected for inclusion. References from the literature search were
imported into Mendeley (v1.19.6, Elsevier, Amsterdam, Netherlands) where duplicates were
removed. Covidence systematic review software (Veritas Health Innovation Ltd, Melbourne,
Australia. Available at www.covidence.org) was used to synthesize and extract eligible studies.
Data extraction and collation
Data was extracted from eligible studies into an evidence table to summarize the following:
year of publication, country, area of body, procedure, health condition, orthopaedic care func-
tion, study design and number of patients. A formal quality appraisal of eligible studies was
not performed as this is beyond the remit of a scoping review. The data collected in the
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Competing interests: The authors have declared
that no competing interests exist.
evidence table was used to define the main themes of research and the summarised data repre-
sented below.
Results
Searches
After removal of duplicates, the search retrieved 3649 documents for title and abstract screen-
ing. Of those, 512 met the eligibility criteria for full text screening and 222 met the final
Fig 1. Literature search and study identification strategy. PRISMA flow diagram showing the search strategy and number of included and
excluded studies.
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inclusion criteria. A reference list of included studies can be found in S4 Table. The study with
the earliest publication date, 1989, used a machine learning method (inductive learning) to
predict operative findings of disc prolapse or nerve entrapment [9]. 139 studies used one AI
technique and 83 used more than one. Machine learning techniques were used 236 times and
deep learning techniques 162 times. The most used machine learning techniques were Support
Vector Machines, 55 times, and Random Forests, 38 times. Of the studies that used deep learn-
ing techniques, 26 implemented convolution layers in their neural networks. Characteristics of
all the studies are summarised below in categories of data extraction.
Imaging
101 studies used AI to interpret an imaging modality to establish a diagnosis. A number of
early papers assessed and quantified the curvature of the spine in scoliosis [10–12], and devel-
oped algorithms capable of calculating the Cobb angle using surface topography before using
radiographs and three-dimensional imaging. Subsequently, AI was applied to the detection of
other spinal pathologies e.g disc herniation or vertebral fractures [13–16]. More recently the
scope of AI to aid diagnostic imaging has expanded outside of the spine, with uses ranging
from the identification of hip fractures to soft tissue meniscal tears in the knee [17–19]. There
has also been a shift to algorithms providing a more nuanced grading of disease, rather than
binary outputs [20].
Orthopaedic care function
106 studies used AI to aid diagnostic decision support and 95 studies used AI to predict an
aspect of a patient’s care. The first paper to use AI in orthopaedics predicted operative findings
during low back surgery [9]. The data comprised of preoperative clinical features and was ana-
lysed using an inductive learning method. More recently, research has focused on algorithms
predicting patient outcomes post-surgery, utilizing the large orthopaedic data sets collected at
local and national level. In particular, two centres in the USA have developed algorithms using
local hospital data across different patient groups and procedures [21–25].
Area of body
96 studies focused on the spine, 51 on the knee, 31 on the hip and 24 involved multiple areas.
Other areas had 5 publications or fewer (Fig 2).
Health condition
68 publications related to spinal pathologies, 64 to trauma and 62 to arthritis. Other conditions
were reported in 5 studies or fewer.
Procedure
141 publications did not relate to a specific orthopaedic procedure. 34 related to arthroplasty
and 26 to spinal procedures. Other procedures were reported in 5 publications or fewer.
Size of dataset used
There was a large range in the size of dataset used in the studies. The largest dataset used
1106234 patients [26], the smallest only 4 [27]. The median number of patients used was 250.
68 studies had a dataset of fewer than 100 patients. Arthroplasty registries were the sources of
some of the larger datasets with information from over 1 million patients being used to build
AI models [23,26,28–31].
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Year of publication
The number of studies has increased in the last half a decade, with 14 publications in 2016 and 70
in 2019 (Fig 3). Between 1989 and 2010 the maximum number of publications per year was 6.
Geographical location
83 studies (37%) were published from the USA, 24 from Canada, 23 from China, 11 from
South Korea and 10 from India. Other countries had fewer than 10 published studies (Fig 4).
Several papers from the USA emanate from the same institution, who have applied similar AI
models to a range of applications [24,32,33].
Discussion
We have reviewed and summarised the characteristics of 222 publications that included AI and
orthopaedics. This scoping review was conducted to establish where and how AI has been used
in orthopaedics. We have described the overarching features of these publications to highlight
where the research has been focused and guide future avenues of research. The predominant
findings were 1) Nearly half of the publications related to imaging interpretation to establish a
diagnosis; 2) The spine was the most studied musculoskeletal region; and 3) Predicting patient
outcomes is an emerging area of interest. Overall, research in AI and orthopaedics is at an early
stage when compared to radiology [34], for example, but entering a phase of significant growth.
AI was used in 101 publications (45%) to interpret an imaging modality to establish a diag-
nosis. This focus can be explained by the large volume of organized data acquired during imag-
ing and the relative ease with which AI models can be built to interpret this data. Radiology,
Fig 2. Publication count by orthopaedic area of interest. A graph showing the number of papers published with regards to the
area of the body.
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accordingly, has seen one of the biggest increases in the use of AI to interpret scans [34]. The
overlap between radiology and orthopaedics, for example, in fracture detection [13] or Cobb
angle measurement from radiographs [35] could also explain the predominance of imaging
related studies.
The initial search identified many publications relating to image segmentation, whereby an
algorithm is used to automatically segment a specific structure(s), such as an intervertebral
disc, from an imaging modality [36]. Papers that described segmentation of normal scans or
were unable to detect pathology were not felt to be of direct clinical relevance and hence were
excluded. Segmentation is, however, an important step in the process of establishing a diagno-
sis from imaging and it is relevant to mention the volume of research to date in this area. The
use of real-time image segmentation with augmented reality is now being used as a navigation
tool in spinal surgery [37], and this technique could be applied elsewhere in orthopaedics.
The spine, hip and knee were the regions most studied. The joint management of spinal
pathology with neurosurgery could explain the greater proportion of papers on the spine.
Large arthroplasty registries could suggest why hip and knee have seen more interest than the
sub-specialty areas of foot & ankle and hand. More research should be focused on sub-spe-
cialty areas other than spine, hip and knee.
Fig 3. Number of papers by year of publication and by country of origin.A graph showing the number of papers published per year and by the country of origin. The
five countries with the most publications are listed. Countries with fewer than 10 publications have been grouped into ‘Other’.
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A significant volume of research found through the literature search related to translational
engineering. A number of studies were published in engineering journals and so may not have
reached readers from a clinical background [38–40]. Comparatively few papers from rheuma-
tology were found in this study [41,42]. This may be due to the inclusion criteria used and the
health conditions of interest. The interplay between different specialties and industries pres-
ents an opportunity to promote interdisciplinary research. Specialists in data science are
needed to progress AI in healthcare, and joint projects between specialties will make future
research more efficient.
AI works best with high quality, large datasets. It was noted that the size of dataset in the
published literature was highly variable. Sixty-eight (31%) of the studies had fewer than 100
patients. Whilst there is no set minimum dataset size for AI algorithms, the reliability of stud-
ies performed using small numbers may be questioned. Registries provided the largest sources
of data in publications identified in this study [23,26,28–31]. They will continue to be a valu-
able resource for further studies predicting personalised patient outcomes. Albeit, there is con-
cern that population-based data may be unable to solve clinical problems at a patient level
[43]. Data sharing is needed for ongoing training and improvement of AI algorithms [2]. Leg-
islation, such as GDPR, ensures that consent for data sharing is obtained and appropriate secu-
rity measures are in place for the storage of data. Data privacy and protection is of utmost
importance going forward.
Fig 4. Number of papers by country of origin. A graph showing the number of papers published by the country of origin of the first author.
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There is scope for AI tools to assist in decision making regarding the management of
patients. AI models that have been developed to retrospectively look at registry data could be
used to design prospective studies. A decision-making aide would be a useful adjunct, for
example, in understanding which patients will have favourable outcomes after arthroplasty.
Predictive models will also provide insights into cost savings and efficiencies that will be of
interest to healthcare providers.
AI is a rapidly advancing discipline with new algorithmic models constantly in develop-
ment, often described using new and different terminology. Machine learning, deep learning
and neural networks are some of the terms encountered in the literature that come under the
umbrella term of AI. This variation in terminology has led to differences in how the papers are
keyworded and recorded in databases. A PubMed (PubMed.gov, National Center for Biotech-
nology Information, Bethesda, MD, USA) search of “Artificial Intelligence Orthopaedics” in
August 2019 yielded a mere 120 results. It was clear that many appropriate papers were missed
and led to refinement of the search strategy for this study. A standardised method of reporting
AI studies is currently lacking and would allow direct assessment and comparison of studies.
Similarly, consistency in terminology and keywords would allow researchers to search for rele-
vant papers more easily. “Artificial Intelligence” is, perhaps, too broad, and not clearly defined
to be used as an umbrella term for keyword searches. We propose that the umbrella term
“Machine learning” should be included on all papers for standardisation.
There was a geographical split in the location of papers published. As represented in Fig 3,
most papers (n = 83) originated from the USA, followed by Canada (n = 24) and China
(n = 23). These results may have been skewed by our inclusion only of papers written in
English but highlights the dominance of institutions from the USA. Additionally, it is impor-
tant to note that the search terms, whilst broader than a previous literature review [1] were not
exhaustive, and despite our best efforts valid publications may have been missed. Some time
has passed since the literature search was performed, and progress has been made in AI in
orthopaedics and more widely in healthcare. Efforts to quantify the diagnostic accuracy of
deep learning in medical imaging and guidelines for reporting such studies are two examples
of how the field has progressed [44,45].
Conclusion
The use of AI in orthopaedics is increasing. Studies using large datasets exist and novel AI
tools with the ability to have clinical impact are being developed. More research is needed
before the potential of AI can translate to a significant change in the day-to-day clinical prac-
tice of orthopaedic surgeons.
Supporting information
S1 Table. PRISMA-ScR checklist.
(DOCX)
S2 Table. Database search terms for Ovid—Embase and Medline.
(DOCX)
S3 Table. Database search terms for Scopus.
(DOCX)
S4 Table. Reference list of papers included in study.
(DOCX)
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Author Contributions
Conceptualization: Gareth G. Jones.
Data curation: Simon J. Federer.
Formal analysis: Simon J. Federer.
Investigation: Simon J. Federer.
Methodology: Simon J. Federer.
Project administration: Gareth G. Jones.
Supervision: Gareth G. Jones.
Writing – original draft: Simon J. Federer.
Writing – review & editing: Gareth G. Jones.
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Artificial intelligence in orthopaedics: A scoping review
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