Available technologies

Deep learning cardiac motion analysis for survival prediction in heart failure

Reference number: 8903

A tool for predicting survival outcomes in patients with pulmonary hypertension with greater accuracy than doctor’s measures.

Proposed use

This technology is suggested for use in pulmonary hypertension, a disease with high mortality in which treatment choice depends on individual risk stratification.

Problem addressed

Doctors suggest different treatment regimes for patients with pulmonary hypertension by making judgements based on a range of measures. Within this method, there is scope for mistakes to be made.

Technology overview

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%

Intellectual property information

The technology is protected by a UK priority patent application, number GB1816281.8

Link to published paper(s)

Bello, G.A., Dawes, T.J.W., Duan, J. et al. Deep-learning cardiac motion analysis for human survival prediction. Nat Mach Intell 1, 95–104 (2019). https://doi.org/10.1038/s42256-019-0019-2

Inventor information

Dr Declan O’Regan, Reader in Imaging Sciences, Faculty of Medicine, Imperial College London

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Jon Wilkinson

Industry Partnerships and Commercialisation Senior Executive, Medicine


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