Abstract
Abstract
Background
To investigate the reproducibility of automated volumetric bone mineral density (vBMD) measurements from routine thoracoabdominal computed tomography (CT) assessed with segmentations by a convolutional neural network and automated correction of contrast phases, on diverse scanners, with scanner-specific asynchronous or scanner-agnostic calibrations.
Methods
We obtained 679 observations from 278 CT scans in 121 patients (77 males, 63.6%) studied from 04/2019 to 06/2020. Observations consisted of two vBMD measurements from Δdifferent reconstruction kernels (n = 169), Δcontrast phases (n = 133), scan Δsessions (n = 123), Δscanners (n = 63), or Δall of the aforementioned (n = 20), and observations lacking scanner-specific calibration (n = 171). Precision was assessed using root-mean-square error (RMSE) and root-mean-square coefficient of variation (RMSCV). Cross-measurement agreement was assessed using Bland-Altman plots; outliers within 95% confidence interval of the limits of agreement were reviewed.
Results
Repeated measurements from Δdifferent reconstruction kernels were highly precise (RMSE 3.0 mg/cm3; RMSCV 1.3%), even for consecutive scans with different Δcontrast phases (RMSCV 2.9%). Measurements from different Δscan sessions or Δscanners showed decreased precision (RMSCV 4.7% and 4.9%, respectively). Plot-review identified 12 outliers from different scan Δsessions, with signs of hydropic decompensation. Observations with Δall differences showed decreased precision compared to those lacking scanner-specific calibration (RMSCV 5.9 and 3.7, respectively).
Conclusion
Automatic vBMD assessment from routine CT is precise across varying setups, when calibrated appropriately. Low precision was found in patients with signs of new or worsening hydropic decompensation, what should be considered an exclusion criterion for both opportunistic and dedicated quantitative CT.
Relevance statement
Automated CT-based vBMD measurements are precise in various scenarios, including cross-session and cross-scanner settings, and may therefore facilitate opportunistic screening for osteoporosis and surveillance of BMD in patients undergoing routine clinical CT scans.
Key Points
Artificial intelligence-based tools facilitate BMD measurements in routine clinical CT datasets.
Automated BMD measurements are highly reproducible in various settings.
Reliable, automated opportunistic osteoporosis diagnostics allow for large-scale application.
Graphical Abstract
Publisher
Springer Science and Business Media LLC
Reference34 articles.
1. NIH Consensus Development Panel on Osteoporosis Prevention, Diagnosis, and Therapy (2001) Osteoporosis prevention, diagnosis, and therapy. JAMA 285:785–795. https://doi.org/10.1001/jama.285.6.785
2. Office of the Surgeon General (US) (2004) Bone health and osteoporosis: A report of the surgeon general. US Department of Health and Human Services, Office of the Surgeon General, Rockville, MD
3. Hernlund E, Svedbom A, Ivergård M et al (2013) Osteoporosis in the european union: Medical management, epidemiology and economic burden. A report prepared in collaboration with the international osteoporosis foundation (iof) and the european federation of pharmaceutical industry associations (efpia). Arch Osteoporos 8:136. https://doi.org/10.1007/s11657-013-0136-1
4. National Osteoporosis Foundation (2002) America’s bone health: The state of osteoporosis and low bone mass in our nation. National Osteoporosis Foundation, Washington (DC)
5. Link TM (2012) Osteoporosis imaging: state of the art and advanced imaging. Radiology 263:3–17. https://doi.org/10.1148/radiol.2631201201