15 research outputs found
Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists
A fully-automated deep learning algorithm matched performance of radiologists
in assessment of knee osteoarthritis severity in radiographs using the
Kellgren-Lawrence grading system.
To develop an automated deep learning-based algorithm that jointly uses
Posterior-Anterior (PA) and Lateral (LAT) views of knee radiographs to assess
knee osteoarthritis severity according to the Kellgren-Lawrence grading system.
We used a dataset of 9739 exams from 2802 patients from Multicenter
Osteoarthritis Study (MOST). The dataset was divided into a training set of
2040 patients, a validation set of 259 patients and a test set of 503 patients.
A novel deep learning-based method was utilized for assessment of knee OA in
two steps: (1) localization of knee joints in the images, (2) classification
according to the KL grading system. Our method used both PA and LAT views as
the input to the model. The scores generated by the algorithm were compared to
the grades provided in the MOST dataset for the entire test set as well as
grades provided by 5 radiologists at our institution for a subset of the test
set.
The model obtained a multi-class accuracy of 71.90% on the entire test set
when compared to the ratings provided in the MOST dataset. The quadratic
weighted Kappa coefficient for this set was 0.9066. The average quadratic
weighted Kappa between all pairs of radiologists from our institution who took
a part of study was 0.748. The average quadratic-weighted Kappa between the
algorithm and the radiologists at our institution was 0.769.
The proposed model performed demonstrated equivalency of KL classification to
MSK radiologists, but clearly superior reproducibility. Our model also agreed
with radiologists at our institution to the same extent as the radiologists
with each other. The algorithm could be used to provide reproducible assessment
of knee osteoarthritis severity
KNEE OSTEOARTHRITIS PREDICTION DRIVEN BY DEEP LEARNING AND THE KELLGREN-LAWRENCE GRADING
Degenerative osteoarthritis of the knee (KOA) affects the knee compartments and worsens over 10–15 years. Knee osteoarthritis is the major cause of activity restrictions and impairment in older persons. Clinicians' expertise affects visual examination interpretation. Hence, achieving early detection requires fast, accurate, and affordable methods. Deep learning (DL) convolutional neural networks (CNN) are the most accurate knee osteoarthritis diagnosis approach. CNNs require a significant amount of training data. Knee X-rays can be analyzed by models that use deep learning to extract the features and reduce number of training cycles. This study suggests the usage of DL system that is based on a trained network on five-class knee X-rays with VGG16, SoftMax (Normal, Doubtful, Mild, Moderate, Severe). Two deep CNNs are used to grade knee OA instantly using the Kellgren-Lawrence (KL) methodology. The experimental analysis makes use of two sets of 1650 different knee X-ray images. Each set consists of 514 normal, 477 doubtful, 232 mild, 221 moderate, and 206 severe cases of osteoarthritis of the knee. The suggested model for knee osteoarthritis (OA) identification and severity prediction using knee X-ray radiographs has a classification accuracy of more than 95%, with training and validation accuracy of 95% and 87%, respectively
Machine learning outperforms clinical experts in classification of hip fractures
Hip fractures are a major cause of morbidity and mortality in the elderly, and incur high health and social care costs. Given projected population ageing, the number of incident hip fractures is predicted to increase globally. As fracture classification strongly determines the chosen surgical treatment, differences in fracture classification influence patient outcomes and treatment costs. We aimed to create a machine learning method for identifying and classifying hip fractures, and to compare its performance to experienced human observers. We used 3659 hip radiographs, classified by at least two expert clinicians. The machine learning method was able to classify hip fractures with 19% greater accuracy than humans, achieving overall accuracy of 92%
Using Machine Learning Tools to Predict the Severity of Osteoarthritis Based on Knee X-Ray Data
Knee osteoarthritis(OA) is a very general joint disease that disturb many people especially people over 60. The severity of pain caused by knee OA is the most important portent to disable. Until now, the bad impact of osteoarthritis on health care and public health systems is still increasing.In this paper, we will build a machine learning model to detect the edge of the knee based on the X-ray image and predict the severity of OA. We use a clustering algorithm and machine learning tools to predict the severity of OA in knee X-ray images. The data is coming from the OsteoArthritis Initiative (OAI). To process the data, we use the clustering method as the first step to do unsupervised learning on the dataset and get clusters from each single X-ray image. For every single image, we can get features. Therefore, we transfer complicate image data into simple data, a vector. Then, we use machine learning tools to analyze the extracted feature data and detect the severity of knee OA. We also built a convolutional neural network (CNN) model to make a comparison between the method we used and deep learning algorithm