AI shows potential for lumbar radiograph interpretation in low-resource settings, but remains exploratory

A narrative review finds deep learning models, including CNNs and GANs, can segment and classify lumbar spine radiographs, with lightweight architectures potentially deployable in resource-limited environments. However, real-world validation and clinical integration challenges p…

• AI models, such as U-Net, ResNet, and GANs, have demonstrated improved performance in segmentation, classification, and curvature analysis on lumbar radiographs.
• The review identifies lightweight architectures as a potential solution for deployment in low-resource settings, where computational power may be limited.
• Authors caution that current evidence is limited by a lack of large-scale, real-world validation and unresolved issues of interpretability.

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