In order to promote the effective development of hospital day surgery mode, a construction method of information management platform that meets the characteristics of day surgery mode is presented. By analyzing the business process of the day surgery mode, the system architecture of the information platform is given; according to the difficulty of the surgical scheduling, the two-stage surgical scheduling algorithm based on the ranking theory is given; by analyzing the day surgery data statistically, a multi-angle surgical index analysis module is provided. The information management of the day surgery mode has been realized, and the work efficiency has been improved. A reasonable day surgery information platform construction can help to optimize the daytime surgical procedure and promote the smooth development of day surgery.
Although transcranial magnetic stimulation (TMS) is widely used in neuromodulation, conventional TMS struggles to achieve both depth and focal specificity. Temporal interference TMS (TI-TMS) offers a promising approach to enhance stimulation depth while reducing the focal area; however, current research remains largely simulation-based, with limited studies on system implementation and experimental validation in rodent deep brain regions. To address this, we developed a TI-TMS system based on a realistic mouse head model using finite element simulation. Electrophysiological recordings of local field potentials (LFPs) in the ventral hippocampal formation (vHPC) were performed to evaluate changes in θ rhythm power spectral density (PSD) and θ-γ phase-amplitude coupling (PAC) following stimulation. The results demonstrated that TI-TMS enhanced θ rhythm power and strengthened θ-γ PAC, indicating effective modulation of deep brain regions. This study establishes a functional TI-TMS system capable of effectively stimulating deep vHPC, providing an experimental basis for its application in precise neuromodulation of subcortical brain areas.
In the field of artificial intelligence (AI) medical imaging, data annotation is a key factor in all AI development. In the traditional manual annotation process, there are prominent problems such as difficult data acquisition, high manual labor intensity, strong professionalism and low labeling quality. Therefore, an intelligent multimodal medical image annotation system is urgently needed to meet the requirements of labeling. Based on the image cloud, West China Hospital of Sichuan University collected the multimodal image data of hospital and allied hospitals, and designed a multi-modal image annotation system through information technology, which integrated various image processing algorithms and AI models to simplify the image data annotation. With the construction of annotation system, the efficiency of data labeling in the hospitals is improved, which provides necessary data support for the AI image research and related industry construction in the hospital, so as to promote the implementation of artificial intelligence industry related to medical images in the hospital.