Children's Hospital of Philadelphia Uses NVIDIA's Open Source AI for Cardiac Care

Children's Hospital of Philadelphia (CHOP) is using open source AI tools to model children's hearts in seconds, with the goal of enabling safer, more precise care for kids with congenital heart disease. According to the announcement, about 1% of all live births involve a congenital heart defect, and no two cases are alike. Historically, the devices surgeons reach for were almost never designed with a specific child in mind.

Dr. Matthew Jolley, a cardiologist and researcher at CHOP, said, “Our job is to find what fits — and modeling lets us do that before anyone goes into the cath lab or operating room.” CHOP’s cardiac modeling service, built on MONAI — an open source medical imaging framework cofounded by NVIDIA — takes the images a child’s care team already has, such as CT scans, MRI, and 3D ultrasound, and produces anatomically precise heart models in just seconds. A workflow that once required four hours of work by a skilled researcher now completes fast enough for routine clinical use.

The approach is spreading. More than 20 children’s hospitals across the U.S. now run cardiac modeling programs. At Boston Children’s Hospital, modeling supports more than half of all cardiac surgeries — roughly 500 cases a year. CHOP expects to reach about 200 modeled cases this year. Where this work began in cardiac care, CHOP is now aiming to apply the same tools across multiple disciplines through the IDEA Lab, part of the hospital’s Morgan Center for Research and Innovation.

CHOP is working with NVIDIA and the open source community to build biomechanics-focused simulation frameworks with Warp that can be brought into Newton, originally intended for simulation-based AI robot training. These frameworks, once integrated with 3D Slicer and SlicerHeart, can help doctors understand tissue material properties that determine how a device will deploy in a given patient. With GPU acceleration, they can reduce the time needed for cardiac device simulation from up to four hours to near real time.

Source: nvidia-newsroom