Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: A multicenter retrospective cohort study

Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: A multicenter retrospective cohort study

Osteoporosis is a prevalent but underdiagnosed condition. As compared to dual-energy X-ray absorptiometry (DXA) measures, we aimed to develop a deep convolutional neural network (DCNN) model to classify osteopenia and osteoporosis with the use of lumbar spine X-ray images. Herein, we developed the D...

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Veröffentlicht in:Bone (New York, N.Y.) N.Y.), 2020-11, Vol.140, p.115561-115561, Article 115561
Hauptverfasser: Zhang, Bin, Yu, Keyan, Ning, Zhenyuan, Wang, Ke, Dong, Yuhao, Liu, Xian, Liu, Shuxue, Wang, Jian, Zhu, Cuiling, Yu, Qinqin, Duan, Yuwen, Lv, Siying, Zhang, Xintao, Chen, Yanjun, Wang, Xiaojia, Shen, Jie, Peng, Jia, Chen, Qiuying, Zhang, Yu, Zhang, Xiaodong, Zhang, Shuixing
Format: Artikel
Sprache:eng
Schlagworte:
Bone mineral density
Deep learning
Dual-energy X-ray absorptiometry
Lumbar spine X-rays
Osteoporosis
Postmenopausal women
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Zusammenfassung:Osteoporosis is a prevalent but underdiagnosed condition. As compared to dual-energy X-ray absorptiometry (DXA) measures, we aimed to develop a deep convolutional neural network (DCNN) model to classify osteopenia and osteoporosis with the use of lumbar spine X-ray images. Herein, we developed the DCNN models based on the training dataset, which comprising 1616 lumbar spine X-ray images from 808 postmenopausal women (aged 50 to 92 years). DXA-derived bone mineral density (BMD) measures were used as the reference standard. We categorized patients into three groups according to DXA BMD T-score: normal (T ≥ −1.0), osteopenia (−2.5 
ISSN:8756-3282
1873-2763
DOI:10.1016/j.bone.2020.115561