Assessing knee OA severity with CNN attention-based end-to-end architectures

This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which incorporates trainable attention modules acting as unsupervised fine-grained detectors of the region of interest (ROI). The proposed attention modules can be applied at different levels and scales across any CNN pipeline helping the network to learn relevant attention patterns over the most informative parts of the image at different resolutions. We test the proposed attention mechanism on existing state-of-the-art CNN architectures as our base models, achieving promising results on the benchmark knee OA datasets from the osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST).Postprint (published version

A review of arthritis diagnosis techniques in artificial intelligence era: Current trends and research challenges

Deep learning, a branch of artificial intelligence, has achieved unprecedented performance in several domains including medicine to assist with efficient diagnosis of diseases, prediction of disease progression and pre-screening step for physicians. Due to its significant breakthroughs, deep learning is now being used for the diagnosis of arthritis, which is a chronic disease affecting young to aged population. This paper provides a survey of recent and the most representative deep learning techniques (published between 2018 to 2020) for the diagnosis of osteoarthritis and rheumatoid arthritis. The paper also reviews traditional machine learning methods (published 2015 onward) and their application for the diagnosis of these diseases. The paper identifies open problems and research gaps. We believe that deep learning can assist general practitioners and consultants to predict the course of the disease, make treatment propositions and appraise their potential benefits

Assessing knee OA severity with CNN attention-based end-to-end architectures

This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which incorporates trainable attention modules acting as unsupervised fine-grained detectors of the region of interest (ROI). The proposed attention modules can be applied at different levels and scales across any CNN pipeline helping the network to learn relevant attention patterns over the most informative parts of the image at different resolutions. We test the proposed attention mechanism on existing state-of-the-art CNN architectures as our base models, achieving promising results on the benchmark knee OA datasets from the osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST). All code from our experiments will be publicly available on the github repository: https://github.com/marc-gorriz/KneeOA-CNNAttentio

Quantifying radiographic knee osteoarthritis severity using deep convolutional neural networks

This paper proposes a new approach to automatically quantify the severity of knee osteoarthritis (OA) from radiographs using deep convolutional neural networks (CNN). Clinically, knee OA severity is assessed using Kellgren & Lawrence (KL) grades, a five point scale. Previous work on automatically predicting KL grades from radiograph images were based on training shallow classifiers using a variety of hand engineered features. We demonstrate that classification accuracy can be significantly improved using deep convolutional neural network models pre-trained on ImageNet and fine-tuned on knee OA images. Furthermore, we argue that it is more appropriate to assess the accuracy of automatic knee OA severity predictions using a continuous distance-based evaluation metric like mean squared error than it is to use classification accuracy. This leads to the formulation of the prediction of KL grades as a regression problem and further improves accuracy. Results on a dataset of X-ray images and KL grades from the Osteoarthritis Initiative (OAI) show a sizable improvement over the current state-of-the-art

Automatic quantification of radiographic knee osteoarthritis severity and associated diagnostic features using deep convolutional neural networks

“Automatic Quantification of Radiographic Knee Osteoarthritis Severity and Associated Diagnostic Features using Deep Convolutional Neural Networks” A. Joseph Antony Due to the increasing prevalence of knee Osteoarthritis (OA), a debilitating kneejoint degradation, and total joint arthoplasty as a serious consequence, there is a need for effective clinical and scientific tools to assess knee OA in its early stages. This thesis investigates the use of machine learning algorithms and deep learning architectures, in particular convolutional neural networks (CNN), to quantify the severity and clinical radiographic features of knee OA. The goal is to offer novel and effective solutions to automatically assess the severity of knee OA achieving on par with human accuracy. Instead of conventional hand-crafted features, it is proposed in this thesis that automatically learning features in a supervised manner can be more effective for fine-grained knee OA image classification. The main contributions of this thesis are as follows. First, the use of off-the-shelf CNNs are investigated for classifying knee OA images through transfer learning by fine-tuning the CNNs. Second, CNNs are trained from scratch to quantify the knee OA severity optimising a weighted ratio of two loss functions: categorical cross entropy and mean-squared error. Third, CNNs are jointly trained to quantify the clinical features of knee OA: joint space narrowing (JSN) and osteophytes along with the KL grades. This improves the overall quantification of knee OA severity producing simultaneous predictions of KL grades, JSN and osteophytes. Two public datasets are used to evaluate the approaches, the OAI and the MOST, with extremely promising results that outperform existing approaches. In summary, this thesis primarily contributes to the field of automated methods for localisation and quantification of radiographic knee OA