The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Deep-learning cardiac motion analysis for human survival prediction
Open accessAbstract
Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimizing the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimized for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients, the predictive accuracy (quantified by Harrell’s C-index) was significantly higher (P = 0.0012) for our model C = 0.75 (95% CI: 0.70–0.79) than the human benchmark of C = 0.59 (95% CI: 0.53–0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival. A fully convolutional neural network is used to create time-resolved three-dimensional dense segmentations of heart images. This dense motion model forms the input to a supervised system called 4Dsurvival that can efficiently predict human survival.
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Self-citationDeep learning interpretation of echocardiograms
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RETRACTED ARTICLE: Artificial intelligence and machine learning in precision and genomic medicine
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RetractedPrecision medicine in the era of artificial intelligence: implications in chronic disease management
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Metadata
- Authors
- Bello, G. A., Dawes, T. J. W., Duan, J., Biffi, C., de Marvao, A., Howard, L. S. G. E., Gibbs, J. S. R., Wilkins, M. R., Cook, S. A., Rueckert, D., & O’Regan, D. P.
- Venue
- Nature Machine Intelligence
- Published
- 2019-02-11
- Volume
- 1
- Issue
- 2
- Pages
- 95-104
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W2894845372
- Cited by
- 228
- References
- 11
- FWCI
- 14.64
- Type
- journal article
- Language
- en
- Open access
- green
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- Bello, G. A., Dawes, T. J. W., Duan, J., Biffi, C., de Marvao, A., Howard, L. S. G. E., Gibbs, J. S. R., Wilkins, M. R., Cook, S. A., Rueckert, D., & O’Regan, D. P. (2019). Deep-learning cardiac motion analysis for human survival prediction. Nature Machine Intelligence. https://doi.org/10.1038/s42256-019-0019-2
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