Effect of patient clinical variables in osteoporosis classification using hip x-rays in deep learning analysis

Norio Yamamoto, Shintaro Sukegawa, Kazutaka Yamashita, Masaki Manabe, Keisuke Nakano, Kiyofumi Takabatake, Hotaka Kawai, Toshifumi Ozaki, Keisuke Kawasaki, Hitoshi Nagatsuka, Yoshihiko Furuki, Takashi Yorifuji

Research output: Contribution to journalArticlepeer-review

Abstract

Background and Objectives: A few deep learning studies have reported that combining image features with patient variables enhanced identification accuracy compared with image-only models. However, previous studies have not statistically reported the additional effect of patient variables on the image-only models. This study aimed to statistically evaluate the osteoporosis identification ability of deep learning by combining hip radiographs with patient variables. Materials and Methods: We collected a dataset containing 1699 images from patients who underwent skeletal-bone-mineral density measurements and hip radiography at a general hospital from 2014 to 2021. Osteoporosis was assessed from hip radiographs using convolutional neural network (CNN) models (ResNet18, 34, 50, 101, and 152). We also investigated ensemble models with patient clinical variables added to each CNN. Accuracy, precision, recall, specificity, F1 score, and area under the curve (AUC) were calculated as performance metrics. Furthermore, we statistically compared the accuracy of the image-only model with that of an ensemble model that included images plus patient factors, including effect size for each performance metric. Results: All metrics were improved in the ResNet34 ensemble model compared with the image-only model. The AUC score in the ensemble model was significantly improved compared with the image-only model (difference 0.004; 95% CI 0.002–0.0007; p = 0.0004, effect size: 0.871). Conclusions: This study revealed the additional effect of patient variables in identification of osteoporosis using deep CNNs with hip radiographs. Our results provided evidence that the patient variables had additive synergistic effects on the image in osteoporosis identification.

Original languageEnglish
Article number846
JournalMedicina (Lithuania)
Volume57
Issue number8
DOIs
Publication statusPublished - Aug 2021

Keywords

  • Convolutional neural network
  • Deep learning
  • Effect size
  • Ensemble model
  • Osteoporosis
  • Patient variables

ASJC Scopus subject areas

  • Medicine(all)

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