Details

Autor(en) / Beteiligte

• Hsieh, Chen-I
• Zheng, Kang
• Lin, Chihung
• Mei, Ling
• Lu, Le
• Li, Weijian
• Chen, Fang-Ping
• Wang, Yirui
• Zhou, Xiaoyun
• Wang, Fakai
• Xie, Guotong
• Xiao, Jing
• Miao, Shun
• Kuo, Chang-Fu

Titel

Automated bone mineral density prediction and fracture risk assessment using plain radiographs via deep learning

Ist Teil von

• Nature communications, 2021-09, Vol.12 (1), p.5472-5472, Article 5472

Ort / Verlag

London: Nature Publishing Group

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• Abstract Dual-energy X-ray absorptiometry (DXA) is underutilized to measure bone mineral density (BMD) and evaluate fracture risk. We present an automated tool to identify fractures, predict BMD, and evaluate fracture risk using plain radiographs. The tool performance is evaluated on 5164 and 18175 patients with pelvis/lumbar spine radiographs and Hologic DXA. The model is well calibrated with minimal bias in the hip (slope = 0.982, calibration-in-the-large = −0.003) and the lumbar spine BMD (slope = 0.978, calibration-in-the-large = 0.003). The area under the precision-recall curve and accuracy are 0.89 and 91.7% for hip osteoporosis, 0.89 and 86.2% for spine osteoporosis, 0.83 and 95.0% for high 10-year major fracture risk, and 0.96 and 90.0% for high hip fracture risk. The tool classifies 5206 (84.8%) patients with 95% positive or negative predictive value for osteoporosis, compared to 3008 DXA conducted at the same study period. This automated tool may help identify high-risk patients for osteoporosis.