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  • JOURNAL ARTICLE
    Dawes TJW, de Marvao A, Shi W, Rueckert D, Cook SA, O'Regan DPet al., 2019,

    Identifying the optimal regional predictor of right ventricular global function: a high-resolution three-dimensional cardiac magnetic resonance study

    , ANAESTHESIA, Vol: 74, Pages: 312-320, ISSN: 0003-2409
  • JOURNAL ARTICLE
    Bello GA, Dawes TJW, Duan J, Biffi C, de Marvao A, Howard LSGE, Gibbs JSR, Wilkins MR, Cook SA, Rueckert D, O'Regan DPet al., 2019,

    Deep learning cardiac motion analysis for human survival prediction.

    , Nat Mach Intell, Vol: 1, Pages: 95-104, ISSN: 2522-5839

    Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising 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 optimised 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 = .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.

  • JOURNAL ARTICLE
    Thomas P, 2019,

    Intrinsic and extrinsic noise of gene expression in lineage trees

    , SCIENTIFIC REPORTS, Vol: 9, ISSN: 2045-2322
  • JOURNAL ARTICLE
    Kuntz J, Thomas P, Stan G-B, Barahona Met al., 2019,

    The exit time finite state projection scheme: bounding exit distributions and occupation measures of continuous-time Markov chains

    We introduce the exit time finite state projection (ETFSP) scheme, atruncation-based method that yields approximations to the exit distribution andoccupation measure associated with the time of exit from a domain (i.e., thetime of first passage to the complement of the domain) of time-homogeneouscontinuous-time Markov chains. We prove that: (i) the computed approximationsbound the measures from below; (ii) the total variation distances between theapproximations and the measures decrease monotonically as states are added tothe truncation; and (iii) the scheme converges, in the sense that, as thetruncation tends to the entire state space, the total variation distances tendto zero. Furthermore, we give a computable bound on the total variationdistance between the exit distribution and its approximation, and we delineatethe cases in which the bound is sharp. We also revisit the related finite stateprojection scheme and give a comprehensive account of its theoreticalproperties. We demonstrate the use of the ETFSP scheme by applying it to twobiological examples: the computation of the first passage time associated withthe expression of a gene, and the fixation times of competing species subjectto demographic noise.

  • JOURNAL ARTICLE
    Clarke JM, Warren LR, Arora S, Barahona M, Darzi AWet al., 2018,

    Guiding interoperable electronic health records through patient-sharing networks

    , npj Digital Medicine, Vol: 1, ISSN: 2398-6352

    Effective sharing of clinical information between care providers is a critical component of a safe, efficient health system. National data-sharing systems may be costly, politically contentious and do not reflect local patterns of care delivery. This study examines hospital attendances in England from 2013 to 2015 to identify instances of patient sharing between hospitals. Of 19.6 million patients receiving care from 155 hospital care providers, 130 million presentations were identified. On 14.7 million occasions (12%), patients attended a different hospital to the one they attended on their previous interaction. A network of hospitals was constructed based on the frequency of patient sharing between hospitals which was partitioned using the Louvain algorithm into ten distinct data-sharing communities, improving the continuity of data sharing in such instances from 0 to 65–95%. Locally implemented data-sharing communities of hospitals may achieve effective accessibility of clinical information without a large-scale national interoperable information system.

  • JOURNAL ARTICLE
    Attard MI, Dawes TJW, de Marvao A, Biffi C, Shi W, Wharton J, Rhodes CJ, Ghataorhe P, Gibbs JSR, Howard LSGE, Rueckert D, Wilkins MR, O'Regan DPet al., 2018,

    Metabolic pathways associated with right ventricular adaptation to pulmonary hypertension: 3D analysis of cardiac magnetic resonance imaging.

    , Eur Heart J Cardiovasc Imaging

    Aims: We sought to identify metabolic pathways associated with right ventricular (RV) adaptation to pulmonary hypertension (PH). We evaluated candidate metabolites, previously associated with survival in pulmonary arterial hypertension, and used automated image segmentation and parametric mapping to model their relationship to adverse patterns of remodelling and wall stress. Methods and results: In 312 PH subjects (47.1% female, mean age 60.8 ± 15.9 years), of which 182 (50.5% female, mean age 58.6 ± 16.8 years) had metabolomics, we modelled the relationship between the RV phenotype, haemodynamic state, and metabolite levels. Atlas-based segmentation and co-registration of cardiac magnetic resonance imaging was used to create a quantitative 3D model of RV geometry and function-including maps of regional wall stress. Increasing mean pulmonary artery pressure was associated with hypertrophy of the basal free wall (β = 0.29) and reduced relative wall thickness (β = -0.38), indicative of eccentric remodelling. Wall stress was an independent predictor of all-cause mortality (hazard ratio = 1.27, P = 0.04). Six metabolites were significantly associated with elevated wall stress (β = 0.28-0.34) including increased levels of tRNA-specific modified nucleosides and fatty acid acylcarnitines, and decreased levels (β = -0.40) of sulfated androgen. Conclusion: Using computational image phenotyping, we identify metabolic profiles, reporting on energy metabolism and cellular stress-response, which are associated with adaptive RV mechanisms to PH.

  • JOURNAL ARTICLE
    Thomas P, Terradot G, Danos V, Weisse AYet al., 2018,

    Sources, propagation and consequences of stochasticity in cellular growth

    , NATURE COMMUNICATIONS, Vol: 9, ISSN: 2041-1723
  • JOURNAL ARTICLE
    Dawes TJW, Cai J, Quinlan M, de Marvao A, Ostrowski PJ, Tokarczuk PF, Watson GMJ, Wharton J, Howard LSGE, Gibbs JSR, Cook SA, Wilkins MR, O'Regan DPet al., 2018,

    Fractal Analysis of Right Ventricular Trabeculae in Pulmonary Hypertension

    , RADIOLOGY, Vol: 288, Pages: 386-395, ISSN: 0033-8419
  • CONFERENCE PAPER
    Altuncu MT, Mayer E, Yaliraki SN, Barahona Met al., 2018,

    From Text to Topics in Healthcare Records: An Unsupervised Graph Partitioning Methodology

    Electronic Healthcare Records contain large volumes of unstructured data,including extensive free text. Yet this source of detailed information oftenremains under-used because of a lack of methodologies to extract interpretablecontent in a timely manner. Here we apply network-theoretical tools to analysefree text in Hospital Patient Incident reports from the National HealthService, to find clusters of documents with similar content in an unsupervisedmanner at different levels of resolution. We combine deep neural networkparagraph vector text-embedding with multiscale Markov Stability communitydetection applied to a sparsified similarity graph of document vectors, andshowcase the approach on incident reports from Imperial College Healthcare NHSTrust, London. The multiscale community structure reveals different levels ofmeaning in the topics of the dataset, as shown by descriptive terms extractedfrom the clusters of records. We also compare a posteriori against hand-codedcategories assigned by healthcare personnel, and show that our approachoutperforms LDA-based models. Our content clusters exhibit good correspondencewith two levels of hand-coded categories, yet they also provide further medicaldetail in certain areas and reveal complementary descriptors of incidentsbeyond the external classification taxonomy.

  • JOURNAL ARTICLE
    Arnaudon A, Holm D, Sommer S, 2018,

    String methods for stochastic image and shape matching

    , Journal of Mathematical Imaging and Vision, Vol: 60, Pages: 953-967, ISSN: 0924-9907

    Matching of images and analysis of shape differences is traditionally pursued by energy minimization of paths of deformations acting to match the shape objects. In the large deformation diffeomorphic metric mapping (LDDMM) framework, iterative gradient descents on the matching functional lead to matching algorithms informally known as Beg algorithms. When stochasticity is introduced to model stochastic variability of shapes and to provide more realistic models of observed shape data, the corresponding matching problem can be solved with a stochastic Beg algorithm, similar to the finite-temperature string method used in rare event sampling. In this paper, we apply a stochastic model compatible with the geometry of the LDDMM framework to obtain a stochastic model of images and we derive the stochastic version of the Beg algorithm which we compare with the string method and an expectation-maximization optimization of posterior likelihoods. The algorithm and its use for statistical inference is tested on stochastic LDDMM landmarks and images.

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