Deep-Learning-Based Detection of Vertebral Fracture and Osteoporosis Using Lateral Spine X-Ray Radiography

Namki Hong, Sang Wouk Cho, Sungjae Shin, Seunghyun Lee, Seol A. Jang, Seunghyun Roh, Young Han Lee, Yumie Rhee, Steven R. Cummings, Hwiyoung Kim, Kyoung Min Kim

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

Osteoporosis and vertebral fractures (VFs) remain underdiagnosed. The addition of deep learning methods to lateral spine radiography (a simple, widely available, low-cost test) can potentially solve this problem. In this study, we develop deep learning scores to detect osteoporosis and VF based on lateral spine radiography and investigate whether their use can improve referral of high-risk individuals to bone-density testing. The derivation cohort consisted of patients aged 50 years or older who underwent lateral spine radiography in Severance Hospital, Korea, from January 2007 to December 2018, providing a total of 26,299 lateral spine plain X-rays for 9276 patients (VF prevalence, 18.6%; osteoporosis prevalence, 40.3%). Two individual deep convolutional neural network scores to detect prevalent VF (VERTE-X pVF score) and osteoporosis (VERTE-X osteo score) were tested on an internal test set (20% hold-out set) and external test set (another hospital cohort [Yongin], 395 patients). VERTE-X pVF, osteo scores, and clinical models to detect prevalent VF or osteoporosis were compared in terms of the areas under the receiver-operating-characteristics curves (AUROCs). Net reclassification improvement (NRI) was calculated when using deep-learning scores to supplement clinical indications for classification of high-risk individuals to dual-energy X-ray absorptiometry (DXA) testing. VERTE-X pVF and osteo scores outperformed clinical models in both the internal (AUROC: VF, 0.93 versus 0.78; osteoporosis, 0.85 versus 0.79) and external (VF, 0.92 versus 0.79; osteoporosis, 0.83 versus 0.65; p < 0.01 for all) test sets. VERTE-X pVF and osteo scores improved the reclassification of individuals with osteoporosis to the DXA testing group when applied together with the clinical indications for DXA testing in both the internal (NRI 0.10) and external (NRI 0.14, p < 0.001 for all) test sets. The proposed method could detect prevalent VFs and osteoporosis, and it improved referral of individuals at high risk of fracture to DXA testing more than clinical indications alone.

Original languageEnglish
Pages (from-to)887-895
Number of pages9
JournalJournal of Bone and Mineral Research
Volume38
Issue number6
DOIs
Publication statusPublished - 2023 Jun

Bibliographical note

Publisher Copyright:
© 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

All Science Journal Classification (ASJC) codes

  • Endocrinology, Diabetes and Metabolism
  • Orthopedics and Sports Medicine

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