Condition: Congenital Heart Disease (CHD) · Ventricular Septal Defects (VSD) · Cardiac Catheterization · Sponsor: Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
The goal of this observational study is to develop and validate a multimodal artificial intelligence prediction model for treatment-related complications in children with perimembranous ventricular septal defect (pmVSD) undergoing transcatheter device closure. The main question it aims to answer is: Can an AI model that integrates demographics, laboratory results, electronic health record text, echocardiography reports, chest radiographs, and electrocardiogram accurately predict the risk of complications at the individual patient level? Data will be retrospectively collected from routine clinical care records of pediatric patients who underwent transcatheter closure for pmVSD. Deep learning methods will be used to extract features from text and images to train and validate the prediction model.
This description comes directly from the study's public registry record.
Kun Sun · 021-13601846338 · drsunkun@xinhuamed.com.cn
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| Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine | Shanghai, Shanghai Municipality, China | Recruiting |
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Source record: clinicaltrials.gov/study/NCT07375602