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Identification of L5 vertebra on lumbar spine radiographs using deep learning
Jeoung Kun Kim1, Min Cheol Chang2, Wook Tae Park3
1Department of Business Administration, School of Business, Yeungnam University, Gyeongsan-si, Republic of Korea.
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.
