Closing the gap in diagnosing and preventing heart disease would have an enormous effect on global health. Researchers at the Imperial College London are using virtual 3D models of the heart and machine learning to do just that. Heart disease is the leading cause of death around the world. Around 24 million people will die each year from heart-related ailments by 2030, according to American Heart Association estimates.
The research team at ICL combined image analysis and machine learning algorithms to model heart contractions — you can see the mesmerizing work in the video below. They then match those models against past patient outcomes to suggest better treatments and potentially improve outcomes in future patients. In a recent research paper, lead author Dr. Declan O’Regan made the case that the solution is more accurate and faster than current methods and, he says, GPUs are a big reason why. “Machine learning has been used to identify the structure of the heart previously, but GPU-accelerated deep learning approaches enable this to be performed in seconds rather than hours, and have also boosted accuracy,” O’Regan said.
