Building personalized models of the heart is a complex challenge. Besides creating a subvoxel-accurate model for each patient, HeartFlow’s system must simulate the flow of blood, vessel by vessel. Diagnosis is also time-sensitive in fast-paced emergency care departments.
“When a patient presents to the emergency room with suspected coronary heart disease, they need a diagnosis fast,” says Leo Grady, senior vice president of engineering at HeartFlow.
To solve this problem, HeartFlow turned to deep learning, accelerated by NVIDIA GPUs. The company adapted conventional deep learning to the analysis of blood vessels using a novel vessel-specific architecture. Its computer vision algorithms analyze medical imaging data to create a personalized 3D model of the patient’s heart and coronary arteries by understanding the information contained in a CT scan.
