Aortic Calcification Artifact Causing Spuriously High Bone Mineral Density in the Lumbar Spine

Gupta, Pragya; Cherian, Kripa Elizabeth; Kapoor, Nitin; Paul, Thomas

doi:10.1016/j.aace.2020.12.007

Cited by 1 publication

(2 citation statements)

References 4 publications

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“…In regards to the BMI, we speculated that the differences in soft tissue information between CT and DXA. In the present study, CT contained only bone information from the cropped lumbar vertebra, whereas DXA contained information on bone, soft tissue, aorta, and other organs 11,24 . In addition, the dose adjustment mechanism based on body size may have affected the prediction of BMD with deep learning.…”

Section: Discussionmentioning

confidence: 99%

“…In the present study, CT contained only bone information from the cropped lumbar vertebra, whereas DXA contained information on bone, soft tissue, aorta, and other organs. 11 , 24 In addition, the dose adjustment mechanism based on body size may have affected the prediction of BMD with deep learning. Computed tomography had an autoexposure control mechanism that adjusted dose modulation based on body size, 25 whereas DXA did not have a dose modulation mechanism.…”

Section: Discussionmentioning

confidence: 99%

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Feasibility of Bone Mineral Density and Bone Microarchitecture Assessment Using Deep Learning With a Convolutional Neural Network

Yoshida,

Tanabe,

Nishiyama

et al. 2023

J Comput Assist Tomogr

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Objectives: We evaluated the feasibility of using deep learning with a convolutional neural network for predicting bone mineral density (BMD) and bone microarchitecture from conventional computed tomography (CT) images acquired by multivendor scanners. Methods:We enrolled 402 patients who underwent noncontrast CT examinations, including L1-L4 vertebrae, and dual-energy x-ray absorptiometry (DXA) examination. Among these, 280 patients (3360 sagittal vertebral images), 70 patients (280 sagittal vertebral images), and 52 patients (208 sagittal vertebral images) were assigned to the training data set for deep learning model development, the validation, and the test data set, respectively. Bone mineral density and the trabecular bone score (TBS), an index of bone microarchitecture, were assessed by DXA. BMD DL and TBS DL were predicted by deep learning with a convolutional neural network (ResNet50). Pearson correlation tests assessed the correlation between BMD DL and BMD, and TBS DL and TBS. The diagnostic performance of BMD DL for osteopenia/osteoporosis and that of TBS DL for bone microarchitecture impairment were evaluated using receiver operating characteristic curve analysis.Results: BMD DL and BMD correlated strongly (r = 0.81, P < 0.01), whereas TBS DL and TBS correlated moderately (r = 0.54, P < 0.01). The sensitivity and specificity of BMD DL for identifying osteopenia or osteoporosis were 93% and 90%, and 100% and 94%, respectively. The sensitivity and specificity of TBS DL for identifying patients with bone microarchitecture impairment were 73% for all values. Conclusions:The BMD DL and TBS DL derived from conventional CT images could identify patients who should undergo DXA, which could be a gatekeeper tool for detecting latent osteoporosis/osteopenia or bone microarchitecture impairment.

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Section: Discussionmentioning

confidence: 99%