Detecting hand joint ankylosis and subluxation in radiographic images using deep learning: A step in the development of an automatic radiographic scoring system for joint destruction.

Journal: PloS one
PMID:

Abstract

We propose a wrist joint subluxation/ankylosis classification model for an automatic radiographic scoring system for X-ray images. In managing rheumatoid arthritis, the evaluation of joint destruction is important. The modified total Sharp score (mTSS), which is conventionally used to evaluate joint destruction of the hands and feet, should ideally be automated because the required time depends on the skill of the evaluator, and there is variability between evaluators. Since joint subluxation and ankylosis are given a large score in mTSS, we aimed to estimate subluxation and ankylosis using a deep neural network as a first step in developing an automatic radiographic scoring system for joint destruction. We randomly extracted 216 hand X-ray images from an electronic medical record system for the learning experiments. These images were acquired from patients who visited the rheumatology department of Keio University Hospital in 2015. Using our newly developed annotation tool, well-trained rheumatologists and radiologists labeled the mTSS to the wrist, metacarpal phalangeal joints, and proximal interphalangeal joints included in the images. We identified 21 X-ray images containing one or more subluxation joints and 42 X-ray images with ankylosis. To predict subluxation/ankylosis, we conducted five-fold cross-validation with deep neural network models: AlexNet, ResNet, DenseNet, and Vision Transformer. The best performance on wrist subluxation/ankylosis classification was as follows: accuracy, precision, recall, F1 value, and AUC were 0.97±0.01/0.89±0.04, 0.92±0.12/0.77±0.15, 0.77±0.16/0.71±0.13, 0.82±0.11/0.72±0.09, and 0.92±0.08/0.85±0.07, respectively. The classification model based on a deep neural network was trained with a relatively small dataset; however, it showed good accuracy. In conclusion, we provided data collection and model training schemes for mTSS prediction and showed an important contribution to building an automated scoring system.

Authors

  • Keisuke Izumi
    Department of Internal Medicine, Division of Rheumatology, Keio University School of Medicine, Tokyo, Japan.
  • Kanata Suzuki
    Fujitsu Laboratories LTD., Kawasaki, Japan.
  • Masahiro Hashimoto
    Department of Radiology, Keio University School of Medicine, Tokyo, Japan. m.hashimoto@rad.med.keio.ac.jp.
  • Toshio Endoh
    Fujitsu Limited, Kanagawa, Japan.
  • Kentaro Doi
    Fujitsu Limited, Kanagawa, Japan.
  • Yuki Iwai
    Fujitsu Limited, Kanagawa, Japan.
  • Masahiro Jinzaki
    Department of Radiology, Keio University School of Medicine, Tokyo, Japan.
  • Shigeru Ko
    Medical AI Center, Keio University School of Medicine, Tokyo, Japan.
  • Tsutomu Takeuchi
    Division of Rheumatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan. tsutake@z5.keio.jp.
  • Yuko Kaneko
    Department of Internal Medicine, Division of Rheumatology, Keio University School of Medicine, Tokyo, Japan.