Clinical Research Directory

TEAS Combined With Triple Antiemetic Drugs to Prevent PONV in High-Risk Patients

The goal of this clinical trial is to evaluate the effects of transcutaneous electrical acupoint stimulation (TEAS) combined with triple antiemetics for postoperative nausea and vomiting (PONV) in high-risk patients. The primary question it seeks to answer is: Does TEAS combined with triple antiemetics further reduce the incidence of PONV in high-risk subjects? Researchers will compare active TEAS with sham stimulation to determine whether the addition of TEAS to dexamethasone, palonosetron, and droperidol lowers the PONV rate beyond that achieved by the triple-drug prophylaxis alone.

Establishment and Clinical Application of AI-based Multimodal Diagnosis System for Ovarian Tumors

Ovarian tumors are a common disease that threatens women's health. They are insidious in onset, have over ten pathological types, and exhibit diverse biological behaviors, making accurate diagnosis a key factor in clinical decision-making and improving prognosis. Introducing AI technology to establish an auxiliary diagnosis system composed of multi-dimensional clinical data, including medical imaging and tumor markers, will greatly enhance diagnosis efficiency by predicting the pathological types of common ovarian tumors. Our research group has innovatively developed an AI-based ultrasound intelligent auxiliary diagnosis software for ovarian tumors, which has been clinically validated to be effective. This project will build on this by: (1) utilizing a wealth of multi-center retrospective clinical data to combine ultrasound, MRI images, physiological, pathological, and laboratory data to form the first multi-modal ovarian tumor public dataset supporting AI tasks; (2) using convolutional neural network technology to realize multi-modal image multi-classification intelligent recognition on this dataset based on surgical pathology as the standard, and then fuse features at the level of clinical data with the intelligent recognition model to train and validate an auxiliary diagnosis model for predicting the top ten pathological types of ovarian tumors; (3) applying privacy computing and federated learning methods to conduct multi-center, prospective validation and optimization of the above model, ultimately forming a clinical auxiliary diagnosis system that can predict the pathological types of most ovarian tumors and apply it to clinical practice.