Purpose
Osteoporosis and osteopenia are extremely common and can lead to fragility fractures.
The purpose of this study was to determine whether a computer learning system could
classify whether a hand radiograph demonstrated osteoporosis based on the second metacarpal
cortical percentage.
Methods
We used the second metacarpal cortical percentage as the osteoporosis predictor. A
total of 4,000 posteroanterior (PA) radiographs of the hand were standardized through
laterality correction, vertical alignment correction, segmentation, proxy osteoporosis
predictor, and full pipeline. Laterality was classified using a LeNet convolutional
neural network (CNN). Vertical alignment classification used 2,000 PA x-rays to determine
vertical alignment of the second metacarpal. We employed segmentation to determine
which pixels belong to the second metacarpal from 1,000 PA x-rays using the FSN-8
CNN. The full pipeline was tested on 265 previously unseen PA x-rays.
Results
Laterality classification accuracy was 99.62%, with a specificity of 100% and sensitivity
of 99.3%. Rotation of the hand within 10° of vertical was accurate in 93.2% of films.
Segmentation was 94.8% accurate. Proxy osteoporosis predictor was 88.4% accurate.
Full pipeline accuracy was 93.9%. In the testing data set, the CNN had a sensitivity
of 82.4% and specificity of 95.7%. In the balanced data set, 6 of 39 osteoporotic
films were classified as nonosteoporotic; sensitivity was 82.4% and specificity, 94.3%.
Conclusions
We have created a series of CNN that can accurately identify osteoporosis from non-osteoporosis.
Furthermore, our CNN is able to make adjustments to images based on laterality and
vertical alignment.
Clinical relevance
Convolutional neural network and computer learning can be used as an adjunct to dual-energy
x-ray absorptiometry scans or to screen and make appropriate referrals for further
workup in patients with suspected osteoporosis.
Key words
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Article info
Publication history
Published online: January 17, 2020
Accepted:
November 22,
2019
Received:
December 27,
2018
Footnotes
No benefits in any form have been received or will be received related directly or indirectly to the subject of this article.
Identification
Copyright
© 2020 by the American Society for Surgery of the Hand. All rights reserved.
