ArticleThe Journal of international medical research2024
Identification of L5 vertebra on lumbar spine radiographs using deep learning.
Article in The Journal of international medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Three-Dimensional, Image-Based Evaluation of the L5 Vertebral Body and Its Ossification Center in Human Fetuses.Brain sciences · 2025Article
- Automated Detection of Lumbosacral Transitional Vertebrae on Plain Lumbar Radiographs Using a Deep Learning Model.Journal of clinical medicine · 2025Article
- Deep learning algorithm for the automatic assessment of axial vertebral rotation in patients with scoliosis using the Nash-Moe method.Scientific reports · 2025Article
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4 authors.
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Abstract
objectiveDeep 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.
methodsWe 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.
resultsThe 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.
conclusionOur deep learning model showed an outstanding ability to identify L5 vertebrae.
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