Journal of Korean Neurosurgical Society

The Korean Neurosurgical Society (대한신경외과학회)
권호 표지
DOI QR코드

DOI QR Code

Machine Learning Model to Predict Osteoporotic Spine with Hounsfield Units on Lumbar Computed Tomography

  • Nam, Kyoung Hyup (Department of Neurosurgery, Medical Research Institute, Pusan National University Hospital) ;
  • Seo, Il (Department of Neurosurgery, Medical Research Institute, Pusan National University Hospital) ;
  • Kim, Dong Hwan (Department of Neurosurgery, Medical Research Institute, Pusan National University Hospital) ;
  • Lee, Jae Il (Department of Neurosurgery, Medical Research Institute, Pusan National University Hospital) ;
  • Choi, Byung Kwan (Department of Neurosurgery, Medical Research Institute, Pusan National University Hospital) ;
  • Han, In Ho (Department of Neurosurgery, Medical Research Institute, Pusan National University Hospital)
  • Received : 2018.09.03
  • Accepted : 2019.01.15
  • Published : 2019.07.01
Download PDF
⟨ Previous Next ⟩

Abstract

Objective : Bone mineral density (BMD) is an important consideration during fusion surgery. Although dual X-ray absorptiometry is considered as the gold standard for assessing BMD, quantitative computed tomography (QCT) provides more accurate data in spine osteoporosis. However, QCT has the disadvantage of additional radiation hazard and cost. The present study was to demonstrate the utility of artificial intelligence and machine learning algorithm for assessing osteoporosis using Hounsfield units (HU) of preoperative lumbar CT coupling with data of QCT. Methods : We reviewed 70 patients undergoing both QCT and conventional lumbar CT for spine surgery. The T-scores of 198 lumbar vertebra was assessed in QCT and the HU of vertebral body at the same level were measured in conventional CT by the picture archiving and communication system (PACS) system. A multiple regression algorithm was applied to predict the T-score using three independent variables (age, sex, and HU of vertebral body on conventional CT) coupling with T-score of QCT. Next, a logistic regression algorithm was applied to predict osteoporotic or non-osteoporotic vertebra. The Tensor flow and Python were used as the machine learning tools. The Tensor flow user interface developed in our institute was used for easy code generation. Results : The predictive model with multiple regression algorithm estimated similar T-scores with data of QCT. HU demonstrates the similar results as QCT without the discordance in only one non-osteoporotic vertebra that indicated osteoporosis. From the training set, the predictive model classified the lumbar vertebra into two groups (osteoporotic vs. non-osteoporotic spine) with 88.0% accuracy. In a test set of 40 vertebrae, classification accuracy was 92.5% when the learning rate was 0.0001 (precision, 0.939; recall, 0.969; F1 score, 0.954; area under the curve, 0.900). Conclusion : This study is a simple machine learning model applicable in the spine research field. The machine learning model can predict the T-score and osteoporotic vertebrae solely by measuring the HU of conventional CT, and this would help spine surgeons not to under-estimate the osteoporotic spine preoperatively. If applied to a bigger data set, we believe the predictive accuracy of our model will further increase. We propose that machine learning is an important modality of the medical research field.

Keywords

References

  1. Bastanlar Y, Ozuysal M : Introduction to machine learning. Methods Mol Biol 1107 : 105-128, 2014 https://doi.org/10.1007/978-1-62703-748-8_7
  2. Cheng Q, Zhu YX, Zhang MX, Li LH, Du PY, Zhu MH : Age and sex effects on the association between body composition and bone mineral density in healthy Chinese men and women. Menopause 19 : 448-455, 2012 https://doi.org/10.1097/gme.0b013e31823a40ba
  3. Choi MK, Kim SM, Lim JK : Diagnostic efficacy of Hounsfield units in spine CT for the assessment of real bone mineral density of degenerative spine: correlation study between T-scores determined by DEXA scan and Hounsfield units from CT. Acta Neurochir (Wien) 158 : 1421-1427, 2016 https://doi.org/10.1007/s00701-016-2821-5
  4. Coe JD, Warden KE, Herzig MA, McAfee PC : Influence of bone mineral density on the fixation of thoracolumbar implants. A comparative study of transpedicular screws, laminar hooks, and spinous process wires. Spine (Phila Pa 1976) 15 : 902-907, 1990 https://doi.org/10.1097/00007632-199009000-00012
  5. Deo RC : Machine learning in medicine. Circulation 132 : 1920-1930, 2015 https://doi.org/10.1161/CIRCULATIONAHA.115.001593
  6. Ebbesen EN, Thomsen JS, Beck-Nielsen H, Nepper-Rasmussen HJ, Mosekilde L : Lumbar vertebral body compressive strength evaluated by dual-energy X-ray absorptiometry, quantitative computed tomography, and ashing. Bone 25 : 713-724, 1999 https://doi.org/10.1016/S8756-3282(99)00216-1
  7. Erickson BJ, Korfiatis P, Akkus Z, Kline TL : Machine learning for medical imaging. Radiographics 37 : 505-515, 2017 https://doi.org/10.1148/rg.2017160130
  8. Forsting M : Machine learning will change medicine. J Nucl Med 58 : 357-358, 2017 https://doi.org/10.2967/jnumed.117.190397
  9. Halvorson TL, Kelley LA, Thomas KA, Whitecloud TS 3rd, Cook SD : Effects of bone mineral density on pedicle screw fixation. Spine (Phila Pa 1976) 19 : 2415-2420, 1994 https://doi.org/10.1097/00007632-199411000-00008
  10. Hu SS : Internal fixation in the osteoporotic spine. Spine (Phila Pa 1976) 22(24 Suppl) : 43S-48S, 1997 https://doi.org/10.1097/00007632-199712151-00008
  11. Jergas M, Breitenseher M, Gluer CC, Black D, Lang P, Grampp S, et al. : Which vertebrae should be assessed using lateral dual-energy X-ray absorptiometry of the lumbar spine. Osteoporos Int 5 : 196-204, 1995 https://doi.org/10.1007/BF02106100
  12. Kotoku J : An introduction to machine learning. Igaku Butsuri 36 : 18-22, 2016
  13. Lee S, Chung CK, Oh SH, Park SB : Correlation between bone mineral density measured by dual-energy X-ray absorptiometry and Hounsfield units measured by diagnostic CT in lumbar spine. J Korean Neurosurg Soc 54 : 384-389, 2013 https://doi.org/10.3340/jkns.2013.54.5.384
  14. Lochmuller EM, Burklein D, Kuhn V, Glaser C, Muller R, Gluer CC, et al. : Mechanical strength of the thoracolumbar spine in the elderly: prediction from in situ dual-energy X-ray absorptiometry, quantitative computed tomography (QCT), upper and lower limb peripheral QCT, and quantitative ultrasound. Bone 31 : 77-84, 2002 https://doi.org/10.1016/S8756-3282(02)00792-5
  15. Masud T, Langley S, Wiltshire P, Doyle DV, Spector TD : Effect of spinal osteophytosis on bone mineral density measurements in vertebral osteoporosis. BMJ 307 : 172-173, 1993 https://doi.org/10.1136/bmj.307.6897.172
  16. Matsukawa K, Abe Y, Yanai Y, Yato Y : Regional Hounsfield unit measurement of screw trajectory for predicting pedicle screw fixation using cortical bone trajectory: a retrospective cohort study. Acta Neurochir (Wien) 160 : 405-411, 2018 https://doi.org/10.1007/s00701-017-3424-5
  17. Mounach A, Abayi DA, Ghazi M, Ghozlani I, Nouijai A, Achemlal L, et al. : Discordance between hip and spine bone mineral density measurement using DXA: prevalence and risk factors. Semin Arthritis Rheum 38 : 467-471, 2009 https://doi.org/10.1016/j.semarthrit.2008.04.001
  18. Nguyen ND, Eisman JA, Center JR, Nguyen TV : Risk factors for fracture in nonosteoporotic men and women. J Clin Endocrinol Metab 92 : 955-962, 2007 https://doi.org/10.1210/jc.2006-1476
  19. Nonaka K, Uchiyama S : Assessment of volumetric bone mineral density and geometry for hip with clinical CT device. Clin Calcium 21 : 1003-1009, 2011
  20. Reynolds RJ, Day SM : The growing role of machine learning and artificial intelligence in developmental medicine. Dev Med Child Neurol 60 : 858-859, 2018 https://doi.org/10.1111/dmcn.13917
  21. Schreiber JJ, Anderson PA, Rosas HG, Buchholz AL, Au AG : Hounsfield units for assessing bone mineral density and strength: a tool for osteoporosis management. J Bone Joint Surg Am 93 : 1057-1063, 2011 https://doi.org/10.2106/JBJS.J.00160
  22. Somashekhar SP, Sepulveda MJ, Puglielli S, Norden AD, Shortliffe EH, Rohit Kumar C, et al. : Watson for oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Ann Oncol 29 : 418-423, 2018 https://doi.org/10.1093/annonc/mdx781
  23. Suzuki K : Pixel-based machine learning in medical imaging. Int J Biomed Imaging 2012 : 792079, 2012 https://doi.org/10.1155/2012/792079
  24. Suzuki K, Yan P, Wang F, Shen D : Machine learning in medical imaging. Int J Biomed Imaging 2012 : 123727, 2012 https://doi.org/10.1155/2012/123727
  25. Yamagata M, Kitahara H, Minami S, Takahashi K, Isobe K, Moriya H, et al. : Mechanical stability of the pedicle screw fixation systems for the lumbar spine. Spine (Phila Pa 1976) 17(3 Suppl) : S51-S54, 1992 https://doi.org/10.1097/00007632-199203001-00011
  26. Younes M, Ben Hammouda S, Jguirim M, Younes K, Zrour S, Bejia I, et al. : Discordance between spine and hip bone mineral density measurement using DXA in osteoporosis diagnosis: prevalence and risk factors. Tunis Med 92 : 1-5, 2014

Cited by

  1. The future of computer-aided diagnostics in chest computed tomography vol.2019, pp.12, 2019, https://doi.org/10.17116/hirurgia201912191
  2. Applications of Machine Learning Using Electronic Medical Records in Spine Surgery vol.16, pp.4, 2019, https://doi.org/10.14245/ns.1938386.193
  3. Internet of Things, Digital Biomarker, and Artificial Intelligence in Spine: Current and Future Perspectives vol.16, pp.4, 2019, https://doi.org/10.14245/ns.1938388.194
  4. Deep learning augments liver stiffness classification in children vol.51, pp.3, 2021, https://doi.org/10.1007/s00247-020-04916-6
  5. Machine Learning Solutions for Osteoporosis-A Review vol.36, pp.5, 2021, https://doi.org/10.1002/jbmr.4292
  6. Artificial Intelligence and Robotics in Spine Surgery vol.11, pp.4, 2021, https://doi.org/10.1177/2192568220915718
  7. Application of artificial intelligence in diagnosis of osteoporosis using medical images: a systematic review and meta-analysis vol.32, pp.7, 2019, https://doi.org/10.1007/s00198-021-05887-6
  8. Quantifying Bone Quality Using Computed Tomography Hounsfield Units in the Mid-sagittal View of the Lumbar Spine vol.151, 2019, https://doi.org/10.1016/j.wneu.2021.04.051
  9. Validation of a gyroscope-based wearable device for real-time position monitoring of patients in a hospital vol.29, pp.4, 2021, https://doi.org/10.3233/thc-202575
  10. A Robust Segmentation Method Based on Improved U-Net vol.53, pp.4, 2019, https://doi.org/10.1007/s11063-021-10531-9
  11. Applications of Machine Learning in Bone and Mineral Research vol.36, pp.5, 2019, https://doi.org/10.3803/enm.2021.1111