Dual-energy X-ray absorptiometry (DXA) enables accurate bone mineral density but requires specialized equipment and protocols. X-ray-based BMD screening offers opportunistic early detection, though prior methods struggle with X-ray intensity variations and demand large datasets. We introduce a cross-modal knowledge distillation BMD prediction framework (CMKD-BMD) fusing X-ray/CT data to enhance X-ray-only BMD prediction. Each single-modal student network employs a multi-scale visual extractor for hierarchical features, an unsupervised graph-based structural learner for anatomical relationships, and an adaptive fusion module to generate a unified representation. The teacher network integrates single-modal representations from students and transfers multimodal knowledge from the teacher to students. Our model outperforms existing methods on both the collected dataset (comprising 1620 X-rays and 280 CT cases) and the publicly available VerSe2019 dataset, demonstrating superior BMD estimation performance. Furthermore, we developed OrthoSim, an orthopedic surgical simulation platform with CMKD-BMD, which has shown promising clinical effectiveness in trial evaluations. Our code is available at https://github.com/KeyueShi/CMKD-BMD.

Inexhaustible web content carries abundant perceptible information beyond text.Unfortunately, most prior efforts in pre-trained Language Models (LMs) ignore such cyberrichness, while few of them only employ plain HTMLs, and crucial information in the rendered web, such as visual, layout, and style, are excluded.Intuitively, those perceptible web information can provide essential intelligence to facilitate content understanding tasks.This study presents an innovative Gestalt Enhanced Markup (GEM) Language Model inspired by Gestalt psychological theory for hosting heterogeneous visual information from the render tree into the language model without requiring additional visual input.Comprehensive experiments on multiple downstream tasks, i.e., web question answering and web information extraction, validate GEM superiority.