Jeoung Kun Kim, Min Cheol Chang, Wook Tae Park, Gun Woo Lee

Identification of L5 vertebra on lumbar spine radiographs using deep learning

  • Biochemistry (medical)
  • Cell Biology
  • Biochemistry
  • General Medicine
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Objective Deep learning is an advanced machine-learning approach that is used in several medical fields. Here, we developed a deep learning model using an object detection algorithm to identify the L5 vertebra on anteroposterior lumbar spine radiographs, and assessed its detection accuracy. Methods We retrospectively recruited 150 participants for whom both anteroposterior whole-spine and lumbar spine radiographs were available. The anteroposterior lumbar spine radiographs of these patients were used as the input data. Of the 150 images, 105 (70%) were randomly selected as the training set, and the remaining 45 (30%) were assigned to the validation set. YOLOv5x, of the YOLOv5 family model, was used to detect the L5 vertebra area. Results The mean average precisions 0.5 and 0.75 of the trained L5 detection model were 99.2% and 96.9%, respectively. The model’s precision was 95.7% and its recall was 97.8%. Furthermore, 93.3% of the validation data were correctly detected. Conclusion Our deep learning model showed an outstanding ability to identify L5 vertebrae.

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