Tiulpin, A., Thevenot, J., Rahtu, E., Lehenkari, P., Saarakkala, S. (2018) Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach. Scientific Reports, 8 (1). doi:10.1038/s41598-018-20132-7
Automatic knee osteoarthritis diagnosis from plain radiographs : a deep learning-based approach
|Author:||Tiulpin, Aleksei1; Thevenot, Jérôme1; Rahtu, Esa2;|
1Research Unit of Medical Imaging, Physics and Technology, University of Oulu
2Department of Signal Processing, Tampere University of Technology
3Institute of Cancer Research and Translational Medicine, Department of Anatomy and Cell Biology, Faculty of Medicine, University of Oulu
4Department of Diagnostic Radiology, Oulu University Hospital
|Online Access:||PDF Full Text (PDF, 1.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe201804096419
|Publish Date:|| 2018-04-09
Knee osteoarthritis (OA) is the most common musculoskeletal disorder. OA diagnosis is currently conducted by assessing symptoms and evaluating plain radiographs, but this process suffers from subjectivity. In this study, we present a new transparent computer-aided diagnosis method based on the Deep Siamese Convolutional Neural Network to automatically score knee OA severity according to the Kellgren-Lawrence grading scale. We trained our method using the data solely from the Multicenter Osteoarthritis Study and validated it on randomly selected 3,000 subjects (5,960 knees) from Osteoarthritis Initiative dataset. Our method yielded a quadratic Kappa coefficient of 0.83 and average multiclass accuracy of 66.71% compared to the annotations given by a committee of clinical experts. Here, we also report a radiological OA diagnosis area under the ROC curve of 0.93. Besides this, we present attention maps highlighting the radiological features affecting the network decision. Such information makes the decision process transparent for the practitioner, which builds better trust toward automatic methods. We believe that our model is useful for clinical decision making and for OA research; therefore, we openly release our training codes and the data set created in this study.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3126 Surgery, anesthesiology, intensive care, radiology
The OAI is a public-private partnership comprised of five contracts (N01- AR-2-2258; N01-AR-2-2259; N01-AR-2- 2260; N01-AR-2-2261; N01-AR-2-2262) funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by the OAI Study Investigators. Private funding partners include Merck Research Laboratories; Novartis Pharmaceuticals Corporation, GlaxoSmithKline; and Pfizer, Inc. Private sector funding for the OAI is managed by the Foundation for the National Institutes of Health. MOST is comprised of four cooperative grants (Felson - AG18820; Torner - AG18832; Lewis - AG18947; and Nevitt - AG19069) funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by MOST study investigators. This manuscript was prepared using MOST data and does not necessarily reflect the opinions or views of MOST investigators. We would like to acknowledge the strategic funding of the University of Oulu.
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