Improving the Outcome of Very Preterm Infants Using Evidence-based Collaborative Quality Improvement: A Multi-center Prospective Study
A multicenter interventional study using evidence-based collaborative quality improvement to reduce mortality and major morbidities of very preterm infants in six neonatal centers in Shanghai
A Study on the Dosage of Bupivacaine for Spinal Block in Cesarean Section at Sub-Plateau Regions
A Prospective Cohort Study Comparing Artificial Intelligence Multimodal Fusion Prediction Models With Conventional Imaging Assessment for the Diagnosis of Pelvic Lymph Node Metastasis in Cervical Cancer
The goal of this prospective cohort study is to learn whether artificial intelligence multimodal fusion prediction models are effective in diagnosing pelvic lymph node metastasis in cervical cancer. The main question it aims to answer is: can artificial intelligence multimodal fusion prediction models improve the accuracy of preoperative diagnosis of pelvic lymph node metastasis in cervical cancer? The researchers compared the AI multimodal fusion prediction model with traditional imaging physician assessments to see if the prediction model could yield more accurate lymph node metastasis determinations. Participants will undergo pelvic MRI after pathologically confirming a diagnosis of cervical cancer, and the results will be used to determine pelvic lymph node metastasis status by the predictive model and the imaging physician, respectively. Subsequent pathology results after surgical lymph node clearance will be used as the gold standard to determine the accuracy of the two preoperative lymph node diagnostic modalities.
100 Clinical Results associated with Obstetrics and Gynecology Hospital of Fudan University
0 Patents (Medical) associated with Obstetrics and Gynecology Hospital of Fudan University
100 Deals associated with Obstetrics and Gynecology Hospital of Fudan University
100 Translational Medicine associated with Obstetrics and Gynecology Hospital of Fudan University