Professor Ye Qi's team has been granted a U.S. patent

Time: 2025-12-17 19:03:00

    Professor Ye Qi and Director Fang Chihua from Zhujiang Hospital of Southern Medical University jointly lead a special project under the Mathematical Tianyuan Fund of the National Natural Science Foundation of China titled "Key Special Project on the Intersection of Mathematics and Healthcare," focusing on mathematical methods and evolutionary modeling for pancreatic cancer surgery planning and postoperative evaluation. This project encompasses the entire process of pancreatic cancer diagnosis and treatment, utilizing novel medical image processing technologies to enhance, segment, and register CT data. By combining differential equation modeling and physics-informed neural networks to simulate tumor evolution, the team has established a complete technical chain for an intelligent diagnosis and treatment system and implemented clinical applications.

    Recently, the core technology of the pancreatic cancer diagnosis and treatment assistance system developed through this project successfully received authorization for a U.S. invention patent. The patent is owned by South China Normal University, with Professor Ye Qi as the first inventor, and Wen Lihui, Chen Jiawei, and Fang Chihua as co-inventors. Among them, Wen Lihui and Chen Jiawei are graduates previously supervised by Professor Ye Qi. The patented technology is named "Medical Image Segmentation Method based on Boosting-Unet Segmentation Network." Addressing the scarcity of annotated data for pancreatic and pancreatic cancer CT images, this technology integrates the Unet neural network with Boosting algorithm concepts to propose a novel neural network algorithm that achieves automated and precise segmentation of pancreatic and pancreatic cancer regions. Related research achievements have also received invention patents in mainland China and Hong Kong, as well as software copyright registration certificates.

    Currently, Professor Ye Qi and Dr. Wang Weibin continue to collaborate closely with Director Fang Chihua, focusing on addressing the clinical challenge of high postoperative recurrence rates in liver cancer. Targeting key issues such as inadequate visualization of small intraoperative cancer foci, inaccurate fusion of multimodal information, and limited precision in intelligent navigation, their research encompasses: constructing personalized liver digital twin models that integrate anatomical and functional information to enable dynamic prediction of tumor boundaries; developing deep learning-based real-time registration of multimodal images and intraoperative perception technologies to enhance understanding of the surgical environment; establishing an augmented reality navigation-guided laparoscopic/robotic intelligent surgical system to achieve precise navigated liver resection; and building a large model-driven AI-Agent system for comprehensive intelligent assistance throughout the entire course of liver cancer diagnosis and treatment. Ultimately, this forms a complete technical solution chain from "boundary analysis—intelligent navigation—functional assessment," promoting the development of liver cancer surgery toward intelligence and precision.