Open AccessA preliminary examination of the diagnostic value of deep learning in hip osteoarthritis
Author(s) -
Yanling Xue,
Rongguo Zhang,
Yufeng Deng,
Kuan Chen,
Tao Jiang
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0178992
Subject(s) - osteoarthritis , deep learning , convolutional neural network , medicine , artificial intelligence , physical therapy , hip pain , physical medicine and rehabilitation , computer science , pathology , alternative medicine
Hip Osteoarthritis (OA) is a common disease among the middle-aged and elderly people. Conventionally, hip OA is diagnosed by manually assessing X-ray images. This study took the hip joint as the object of observation and explored the diagnostic value of deep learning in hip osteoarthritis. A deep convolutional neural network (CNN) was trained and tested on 420 hip X-ray images to automatically diagnose hip OA. This CNN model achieved a balance of high sensitivity of 95.0% and high specificity of 90.7%, as well as an accuracy of 92.8% compared to the chief physicians. The CNN model performance is comparable to an attending physician with 10 years of experience. The results of this study indicate that deep learning has promising potential in the field of intelligent medical image diagnosis practice.