Google and Verily Life Sciences shared the latest advance in computer vision to identify signs of heart disease. With an accuracy of 70 percent, early results from the AI trained on retinal scan images from more than 200,000 patients is as precise as methods that require blood tests for cholesterol, said Google Brain product manager Lily Peng. It's the latest example of AI being used to tackle the biggest killer in the world: heart disease. It takes more lives than any other cause of death -- 800,000 in the United States alone, according to the American Heart Association. To save lives, an AI army is joining the fight.

The Second National Data Science Bowl, a data science competition where the goal was to automatically determine cardiac volumes from MRI scans, has just ended. We participated with a team of 4 members from the Data Science lab at Ghent University in Belgium and finished 2nd! The team kunsthart (artificial heart in English) consisted of Ira Korshunova, Jeroen Burms, Jonas Degrave (@317070), 3 PhD students, and professor Joni Dambre. It's also a follow-up of last year's team Deep Sea, which finished in first place for the First National Data Science Bowl. This blog post is going to be long, here is a clickable overview of different sections. The goal of this year's Data Science Bowl was to estimate minimum (end-systolic) and maximum (end-diastolic) volumes of the left ventricle from a set of MRI-images taken over one heartbeat. These volumes are used by practitioners to compute an ejection fraction: fraction of outbound blood pumped from the heart with each heartbeat.

The Second National Data Science Bowl, a data science competition where the goal was to automatically determine cardiac volumes from MRI scans, has just ended. We participated with a team of 4 members from the Data Science lab at Ghent University in Belgium and finished 2nd! The team kunsthart (artificial heart in English) consisted of Ira Korshunova, Jeroen Burms, Jonas Degrave (@317070), 3 PhD students, and professor Joni Dambre. It's also a follow-up of last year's team Deep Sea, which finished in first place for the First National Data Science Bowl. This blog post is going to be long, here is a clickable overview of different sections. The goal of this year's Data Science Bowl was to estimate minimum (end-systolic) and maximum (end-diastolic) volumes of the left ventricle from a set of MRI-images taken over one heartbeat. These volumes are used by practitioners to compute an ejection fraction: fraction of outbound blood pumped from the heart with each heartbeat.