Sensor-based X-ray imaging, made possible by high-sensitivity medical imaging sensors equipped with advanced detector materials, enables noninvasive and low-dose assessment of bone structures. Leveraging such sensor outputs, we applied deep learning and explainable AI methods to develop a sensor-driven module for osteoporosis detection. Three hundred fifty-nine hip X-ray images (hereafter referred to as sensor-based X-ray images) were collected to train and evaluate four convolutional neural networks. Among them, the unsegmented whole hip VGG16 model achieved the highest classification accuracy of 76%. Gradient-weighted class activation mapping (Grad-CAM), shapley additive explanations (SHAP), and local interpretable model-agnostic explanations (LIME) were applied to generate saliency heat maps, followed by pixel-level consensus analysis to enhance interpretability. Retraining using the consensus-cropped images increased accuracy to 79% with corresponding 2–3% improvements in sensitivity, specificity, and F1-score. A web-based survey with orthopedic surgeons showed that the visual explanations improved clinical trust in 70% of cases. These results demonstrate that advanced sensor-based X-ray imaging technologies, combined with explainable deep learning, can serve as a practical decision-support module within intelligent medical sensing platforms, especially where dual-energy X-ray absorptiometry (DXA) is unavailable.