Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach
By Aleksei Tiulpin, Jérôme Thevenot, Esa Rahtu, Petri Lehenkari, Simo Saarakkala. 2017
574 citations recorded by the source corpus.
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.
Subfields: Rheumatology
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Citations in the corpus
- Concept Bottleneck Models (2020, neutral): “Neural networks similar to ours have been previously noted to be comparable with individual radiologist performance on grading KLG (compared to the consensus grade, which we use as ground truth; see Tiulpin et al. (2018); Pierson et al. (2019)).”
- Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks (2018, methodology): “In the domain of medical imaging, convolutional neural networks (CNN) have been successfully used, for example, for breast cancer histology image analysis [17], bone disease prediction [18] and age assessment [19], and other problems [15].”
- Breast Tumor Cellularity Assessment using Deep Neural Networks (2019, methodology): “ly to predict the cellularity score. 2. Related work CNNs have recently been successfully applied to many tasks in biomedical image analysis, often outperforming conventional machine learning methods [9,41,35]. As such, they have successfully been utilized for digital pathology image analysis and ha”
- Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks (2017, methodology): “In the domain of medical imaging, convolutional neural networks (CNN) have been successfully used, for example, for diabetic retinopathy screening [15], for breast cancer histology image analysis [16], bone disease prediction [25], and other problems [1].”
- Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning (2018, methodology): “In the domain of medical imaging, convolutional neural networks (CNN) have been successfully used, for example, for breast cancer histology image analysis [17], bone disease prediction [21] and age assessment [10], and other problems [5].”
- Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis (2018, unknown): “2.3”