Evaluation of fragility fracture risk using deep learning based on ultrasound radio frequency signal

Luo, Wenqiang1,2; Wu, Jionglin1,2; Chen, Zhiwei3; Guo, Peidong1,2; Zhang, Qi4,5; Lei, Baiying3; Chen, Zhong1,2; Li, Shixun1,2; Li, Changchuan1,2; Liu, Haoxian1,2; Ma, Teng4,5; Liu, Jiang6; Chen, Xiaoyi7; Ding, Yue1,2

2024-07-01

10.1007/s12020-024-03931-z

ENDOCRINE   影响因子和分区

1355-008X

1559-0100

BackgroundIt was essential to identify individuals at high risk of fragility fracture and prevented them due to the significant morbidity, mortality, and economic burden associated with fragility fracture. The quantitative ultrasound (QUS) showed promise in assessing bone structure characteristics and determining the risk of fragility fracture.AimsTo evaluate the performance of a multi-channel residual network (MResNet) based on ultrasonic radiofrequency (RF) signal to discriminate fragility fractures retrospectively in postmenopausal women, and compared it with the traditional parameter of QUS, speed of sound (SOS), and bone mineral density (BMD) acquired with dual X-ray absorptiometry (DXA).MethodsUsing QUS, RF signal and SOS were acquired for 246 postmenopausal women. An MResNet was utilized, based on the RF signal, to categorize individuals with an elevated risk of fragility fracture. DXA was employed to obtain BMD at the lumbar, hip, and femoral neck. The fracture history of all adult subjects was gathered. Analyzing the odds ratios (OR) and the area under the receiver operator characteristic curves (AUC) was done to evaluate the effectiveness of various methods in discriminating fragility fracture.ResultsAmong the 246 postmenopausal women, 170 belonged to the non-fracture group, 50 to the vertebral group, and 26 to the non-vertebral fracture group. MResNet was competent to discriminate any fragility fracture (OR = 2.64; AUC = 0.74), Vertebral fracture (OR = 3.02; AUC = 0.77), and non-vertebral fracture (OR = 2.01; AUC = 0.69). After being modified by clinical covariates, the efficiency of MResNet was further improved to OR = 3.31-4.08, AUC = 0.81-0.83 among all fracture groups, which significantly surpassed QUS-SOS (OR = 1.32-1.36; AUC = 0.60) and DXA-BMD (OR = 1.23-2.94; AUC = 0.63-0.76).ConclusionsThis pilot cross-sectional study demonstrates that the MResNet model based on the ultrasonic RF signal shows promising performance in discriminating fragility fractures in postmenopausal women. When incorporating clinical covariates, the efficiency of the modified MResNet is further enhanced, surpassing the performance of QUS-SOS and DXA-BMD in terms of OR and AUC. These findings highlight the potential of the MResNet as a promising approach for fracture risk assessment. Future research should focus on larger and more diverse populations to validate these results and explore its clinical applications.

Fragility fracture Quantitative ultrasound Deep learning Radio frequency

SCI

英语

通讯

Bioland Laboratory Independent Project Foundation[ZL01022302] ; Sun Yat-Sen University Clinical Research 5010 Program[2018006]

Endocrinology & Metabolism

Endocrinology & Metabolism

WOS:001265175500001

SPRINGER

BIOLOGY & BIOCHEMISTRY

Web of Science

1.Sun Yat Sen Univ, Sun Yat Sen Mem Hosp, Dept Orthoped Surg, Guangzhou 510120, Peoples R China
2.Bioland Lab, Guangzhou 510320, Peoples R China
3.Shenzhen Univ, Natl Reg Key Technol Engn Lab Med Ultrasound, Guangdong Key Lab Biomed Measurements & Ultrasound, Sch Biomed Engn,Hlth Sci Ctr, Shenzhen 518060, Peoples R China
4.Chinese Acad Sci, Paul C Lauterbur Res Ctr Biomed Imaging, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China
5.Chinese Acad Sci, Natl Innovat Ctr Adv Med Devices, Key Lab Biomed Imaging Sci & Syst, Shenzhen 518126, Peoples R China
6.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China
7.Guoke Ningbo Life Sci & Hlth Ind Res Inst, Ningbo 315000, Peoples R China

Luo, Wenqiang,Wu, Jionglin,Chen, Zhiwei,et al. Evaluation of fragility fracture risk using deep learning based on ultrasound radio frequency signal[J]. ENDOCRINE,2024.

Luo, Wenqiang.,Wu, Jionglin.,Chen, Zhiwei.,Guo, Peidong.,Zhang, Qi.,...&Ding, Yue.(2024).Evaluation of fragility fracture risk using deep learning based on ultrasound radio frequency signal.ENDOCRINE.

Luo, Wenqiang,et al."Evaluation of fragility fracture risk using deep learning based on ultrasound radio frequency signal".ENDOCRINE (2024).