Convolutional Neural Network for Second Metacarpal Radiographic Osteoporosis Screening
| Autor: | Warren C. Hammert, Michael R. Morris, Numair Sani, Jack Teitel, Nahom Tecle, David J. Mitten |
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| Rok vydání: | 2020 |
| Předmět: |
Radiography
Osteoporosis 030230 surgery Convolutional neural network 03 medical and health sciences Absorptiometry Photon 0302 clinical medicine medicine Humans Orthopedics and Sports Medicine Segmentation 030222 orthopedics business.industry Pattern recognition Metacarpal Bones Hand medicine.disease Osteoporosis screening Osteopenia Data set Laterality Surgery Neural Networks Computer Artificial intelligence business |
| Zdroj: | The Journal of Hand Surgery. 45:175-181 |
| ISSN: | 0363-5023 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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