Deep learning cardiac motion analysis for survival prediction in heart failure
Imperial College London
Computing; Calculating; Counting
Bello Ghalib A [Gb]; Biffi Carlo [Gb]; Dawes Timothy J W [Gb]; Duan Jinming [Gb]; O'Regan Declan P [Gb]; Rueckert Daniel [Gb]
About the technology
A tool for predicting survival outcomes in patients with pulmonary hypertension with greater accuracy than doctor’s measures
The technology is a deep-learning algorithm that is trained to find correspondence between heart motion and patient outcome, and which can efficiently predict human survival. Motion analysis is a technique used in computer vision to understand the behaviour of moving objects in sequences of images. It is possible to predict future events based on the current state of a moving 3D scene by learning correspondences between patterns of motion and subsequent outcomes. Imperial researchers used machine learning techniques to analyse the motion dynamics of the beating heart and created a network – 4Dsurvival – which predicts survival outcomes in pulmonary hypertension patients with greater accuracy than doctors’ measures.
- In a study of 302 patients, the accuracy of survival predictions for 4Dsurvival was 75%, significantly higher than the human benchmark of 59%
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About the Research Organization
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