JOURNAL ARTICLE
Add like
Add dislike
Add to saved papers

Using deep transfer learning to detect scoliosis and spondylolisthesis from x-ray images.

Recent years have witnessed wider prevalence of vertebral column pathologies due to lifestyle changes, sedentary behaviors, or injuries. Spondylolisthesis and scoliosis are two of the most common ailments with an incidence of 5% and 3% in the United States population, respectively. Both of these abnormalities can affect children at a young age and, if left untreated, can progress into severe pain. Moreover, severe scoliosis can even lead to lung and heart problems. Thus, early diagnosis can make it easier to apply remedies/interventions and prevent further disease progression. Current diagnosis methods are based on visual inspection by physicians of radiographs and/or calculation of certain angles (e.g., Cobb angle). Traditional artificial intelligence-based diagnosis systems utilized these parameters to perform automated classification, which enabled fast and easy diagnosis supporting tools. However, they still require the specialists to perform error-prone tedious measurements. To this end, automated measurement tools were proposed based on processing techniques of X-ray images. In this paper, we utilize advances in deep transfer learning to diagnose spondylolisthesis and scoliosis from X-ray images without the need for any measurements. We collected raw data from real X-ray images of 338 subjects (i.e., 188 scoliosis, 79 spondylolisthesis, and 71 healthy). Deep transfer learning models were developed to perform three-class classification as well as pair-wise binary classifications among the three classes. The highest mean accuracy and maximum accuracy for three-class classification was 96.73% and 98.02%, respectively. Regarding pair-wise binary classification, high accuracy values were achieved for most of the models (i.e., > 98%). These results and other performance metrics reflect a robust ability to diagnose the subjects' vertebral column disorders from standard X-ray images. The current study provides a supporting tool that can reasonably help the physicians make the correct early diagnosis with less effort and errors, and reduce the need for surgical interventions.

Full text links

For the best experience, use the Read mobile app

Group 7SearchHeart failure treatmentPapersTopicsCollectionsEffects of Sodium-Glucose Cotransporter 2 Inhibitors for the Treatment of Patients With Heart Failure Importance: Only 1 class of glucose-lowering agents-sodium-glucose cotransporter 2 (SGLT2) inhibitors-has been reported to decrease the risk of cardiovascular events primarily by reducingSeptember 1, 2017: JAMA CardiologyAssociations of albuminuria in patients with chronic heart failure: findings in the ALiskiren Observation of heart Failure Treatment study.CONCLUSIONS: Increased UACR is common in patients with heart failure, including non-diabetics. Urinary albumin creatininineJul, 2011: European Journal of Heart FailureRandomized Controlled TrialEffects of Liraglutide on Clinical Stability Among Patients With Advanced Heart Failure and Reduced Ejection Fraction: A Randomized Clinical Trial.Review

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

Read by QxMD is copyright © 2021 QxMD Software Inc. All rights reserved. By using this service, you agree to our terms of use and privacy policy.

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app