Artificial intelligence in lumbar radiography: bridging deep learning and clinical practice in low-resource environments
Yu-Li Wang, Shuwei Huang, Kuei-Chen Lee, Chao‐Min ChengBackground
Artificial intelligence (AI) has increasingly been applied to medical imaging, yet its role in lumbar spine radiography, particularly in low-resource settings, remains underexplored.
Objective
To evaluate recent developments in AI-based approaches for lumbar spine radiography and their clinical applicability in resource-constrained environments.
Methods
A narrative review was conducted focusing on deep learning models applied to lumbar radiographic analysis. Studies published between 2022 and 2024 were identified through structured screening of PubMed and Google Scholar.
Results
Deep learning models, including convolutional neural networks, U-Net, ResNet and generative adversarial networks, have demonstrated improved performance in segmentation, classification and curvature analysis. Lightweight architectures show potential for deployment in resource-limited settings.
Conclusion
AI-based lumbar imaging has the potential to enhance diagnostic accuracy and workflow efficiency in low-resource environments. However, challenges related to validation, interpretability and clinical integration remain, highlighting the need for further large-scale and real-world studies.