Speakers: Jieqiong Wang, PhD

Jieqiong Wang, PhD

Assistant Professor, Department of Neurological Sciences
University of Nebraska Medical Center

 

Dr. Wang is an Assistant Professor in the Department of Neurological Sciences at the University of Nebraska Medical Center. Her research focuses on developing machine learning–based computational models to address critical biomedical challenges in clinical settings. She has collaborated extensively with radiologists, psychologists, neuroscientists, and clinicians on interdisciplinary projects spanning congenital heart disease, cancer, Alzheimer’s disease, and addiction. Her lab has developed a range of AI/ML approaches to tackle key problems in cancer research, including reducing health disparities, cross-modality synthesis, survival prediction, and cancer subtype identification. With over 15 years of experience in artificial intelligence, machine learning, and medical imaging, Dr. Wang has published more than 50 peer-reviewed articles in leading journals such as Advanced Science, Human Brain Mapping, and European Radiology. Her research has been supported by multiple funding agencies, including the NIH (NIGMS, NCI, and OD), NSF, the American Cancer Society, and the Nebraska Research Initiative.

 

Advancing Prenatal Congenital Heart Disease Screening through Artificial Intelligence

Prenatal detection of congenital heart disease (CHD) is often time-consuming, highly dependent on clinician experience, and prone to missed diagnoses due to the complexity of fetal cardiac anatomy and variability in ultrasound image quality. Artificial intelligence (AI) offers a promising solution. In this presentation, we introduce a novel AI framework for prenatal CHD screening that integrates multi-view fetal ultrasound data, capturing complementary anatomical information across standard cardiac views. We will also explore the model’s potential for CHD subtyping and provide interpretable outputs to support clinician decision-making. Our approach aims to reduce clinician burden, enable earlier and more reliable CHD detection, and ultimately improve maternal and fetal outcomes.