Abstract
Background
Osteoarthritis (OA) is the leading cause of disability among adults in the United
States. As the diagnosis is based on the accurate interpretation of knee radiographs,
use of a convolutional neural network (CNN) to grade OA severity has the potential
to significantly reduce variability.
Methods
Knee radiographs from consecutive patients presenting to a large academic arthroplasty
practice were obtained retrospectively. These images were rated by 4 fellowship-trained
knee arthroplasty surgeons using the International Knee Documentation Committee (IKDC)
scoring system. The intraclass correlation coefficient (ICC) for surgeons alone and
surgeons with a CNN that was trained using 4755 separate images were compared.
Results
Two hundred eighty-eight posteroanterior flexion knee radiographs (576 knees) were
reviewed; 131 knees were removed due to poor quality or prior TKA. Each remaining
knee was rated by 4 blinded surgeons for a total of 1780 human knee ratings. The ICC
among the 4 surgeons for all possible IKDC grades was 0.703 (95% confidence interval
[CI] 0.667-0.737). The ICC for the 4 surgeons and the trained CNN was 0.685 (95% CI
0.65-0.719). For IKDC D vs any other rating, the ICC of the 4 surgeons was 0.713 (95%
CI 0.678-0.746), and the ICC of 4 surgeons and CNN was 0.697 (95% CI 0.663-0.73).
Conclusions
A CNN can identify and classify knee OA as accurately as a fellowship-trained arthroplasty
surgeon. This technology has the potential to reduce variability in the diagnosis
and treatment of knee OA.
Keywords
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References
- The impact of osteoarthritis in the United States: a population-health perspective.Am J Nurs. 2012; 112: S13-S19
- The global burden of hip and knee osteoarthritis: estimates from the Global Burden of Disease 2010 study.Ann Rheum Dis. 2014; 73: 1323-1330
- Use of a validated algorithm to judge the appropriateness of total knee arthroplasty in the United States: a multicenter longitudinal cohort study.Arthritis Rheumatol (Hoboken, NJ). 2014; 66: 2134-2143
- Deep learning.Nature. 2015; 521: 436-444
- A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis.Lancet Digital Health. 2019; 1: e271-e297
- Machine learning in orthopedics: a literature review.Front Bioeng Biotechnol. 2018; 6: 75-95
- Osteoarthritis classification scales: interobserver reliability and arthroscopic correlation.J Bone Joint Surg Am. 2014; 96: 1145-1151
- Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care?.J Arthroplasty. 2018; 33: 2358-2361
- Artificial intelligence in musculoskeletal imaging: current status and future directions.Am J Roentgenol. 2019; 213: 506-513
- Ethical dimensions of using artificial intelligence in health care.AMA J Ethics. 2019; 21: 121-124
- Predicting the future—big data, machine learning, and clinical medicine.N Engl J Med. 2016; 375: 1216-1219
- Dermatologist-level classification of skin cancer with deep neural networks.Nature. 2017; 542: 115-118
- Should Watson be consulted for a second opinion?.AMA J Ethics. 2019; 21: 131-137
- Validation of statistical shape modelling to predict hip osteoarthritis in females: data from two prospective cohort studies (Cohort Hip and Cohort Knee and Chingford).Rheumatology. 2015; 54: 2033-2041
- Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs.Eur Radiol. 2019; 29: 5469-5477
- Deep learning predicts hip fracture using confounding patient and healthcare variables.NPJ Digital Med. 2019; 2: 31-41
- Artificial intelligence for analyzing orthopedic trauma radiographs.Acta Orthop. 2017; 88: 581-586
- Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet.PLoS Med. 2018; 15: e1002699-e1002734
- A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis.PLoS One. 2017; 12: e0178992-e0179021
- Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks.in: 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, New York, NY2016: 1195-1201
- Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach.Sci Rep. 2018; 8: 1727-1787
Article info
Publication history
Published online: April 25, 2020
Accepted:
April 19,
2020
Received in revised form:
April 13,
2020
Received:
December 19,
2019
Footnotes
Investigation was performed at the Mayo Clinic Arizona, Phoenix, AZ.
One or more of the authors of this paper have disclosed potential or pertinent conflicts of interest, which may include receipt of payment, either direct or indirect, institutional support, or association with an entity in the biomedical field which may be perceived to have potential conflict of interest with this work. For full disclosure statements refer to https://doi.org/10.1016/j.arth.2020.04.059.
Source of Funding: This research made possible in part by the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery.
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