Dongyang Li

TeethGNN is developed, a novel graph-based framework designed to combine CBCT image features with morphological information for accurate and efficient malocclusion grading, providing an accurate and reliable solution for vision-based clinical measurement and diagnosis.

Diffusion models can now generate high-fidelity virtual contrast-enhanced CT scans from non-contrast scans, potentially reducing the need for invasive contrast agents and radiation exposure.

Multi-agent systems for automated research face a fundamental trade-off: parallel exploration offers speed and stability, while expert teams unlock deeper reasoning at the cost of increased fragility.