Results
- Showing results for:
- Reset all filters
Search results
-
Journal articleHerrero Martin C, Oved A, Chowdhury R, et al.,
EP-PINNs: cardiac electrophysiology characterisation using physics-informed neural networks
, Frontiers in Cardiovascular Medicine, ISSN: 2297-055XAccurately inferring underlying electrophysiological (EP) tissue properties from action potential recordings is expected to be clinically useful in the diagnosis and treatment of arrhythmias such as atrial fibrillation, but it is notoriously difficult to perform. We present EP-PINNs (Physics-Informed Neural Networks), a novel tool for accurate action potential simulation and EP parameter estimation, from sparse amounts of EP data. We demonstrate, using 1D and 2D in silico data, how EP-PINNs are able to reconstruct the spatio-temporal evolution of action potentials, whilst predicting parameters related to action potential duration (APD), excitability and diffusion coefficients. EP-PINNs are additionally able to identify heterogeneities in EP properties, making them potentially useful for the detection of fibrosis and other localised pathology linked to arrhythmias. Finally, we show EP-PINNs effectiveness on biological in vitro preparations, by characterising the effect of anti-arrhythmic drugs on APD using optical mapping data. EP-PINNs are a promising clinical tool for the characterisation and potential treatment guidance of arrhythmias.
-
Conference paperDai T, Liu H, Arulkumaran K, et al., 2021,
Diversity-based trajectory and goal selection with hindsight experience replay
, 18th Pacific Rim International Conference on Artificial Intelligence (PRICAI), Publisher: Springer, Pages: 32-45Hindsight experience replay (HER) is a goal relabelling technique typicallyused with off-policy deep reinforcement learning algorithms to solvegoal-oriented tasks; it is well suited to robotic manipulation tasks thatdeliver only sparse rewards. In HER, both trajectories and transitions aresampled uniformly for training. However, not all of the agent's experiencescontribute equally to training, and so naive uniform sampling may lead toinefficient learning. In this paper, we propose diversity-based trajectory andgoal selection with HER (DTGSH). Firstly, trajectories are sampled according tothe diversity of the goal states as modelled by determinantal point processes(DPPs). Secondly, transitions with diverse goal states are selected from thetrajectories by using k-DPPs. We evaluate DTGSH on five challenging roboticmanipulation tasks in simulated robot environments, where we show that ourmethod can learn more quickly and reach higher performance than otherstate-of-the-art approaches on all tasks.
-
Journal articlePatel R, Thong EHE, Batta V, et al., 2021,
Automated Identification of Orthopedic Implants on Radiographs Using Deep Learning
, RADIOLOGY-ARTIFICIAL INTELLIGENCE, Vol: 3, ISSN: 2638-6100- Author Web Link
- Cite
- Citations: 7
-
Journal articleLiu Y, Zou Z, Yang Y, et al., 2021,
Efficient Source Camera Identification with Diversity-Enhanced Patch Selection and Deep Residual Prediction
, SENSORS, Vol: 21- Author Web Link
- Cite
- Citations: 12
-
Conference paperRodrigues J, Bharath A, Overby D, 2021,
Automated machine learning detection of transcellular pores in Schlemm's canal endothelial cells exposed to stretch
, Annual Meeting of the Association-for-Research-in-Vision-and-Ophthalmology (ARVO), Publisher: ASSOC RESEARCH VISION OPHTHALMOLOGY INC, ISSN: 0146-0404 -
Journal articleDavis BM, Guo L, Ravindran N, et al., 2020,
Dynamic changes in cell size and corresponding cell fate after optic nerve injury
, Scientific Reports, Vol: 10, ISSN: 2045-2322Identifying disease-specific patterns of retinal cell loss in pathological conditions has been highlighted by the emergence of techniques such as Detection of Apoptotic Retinal Cells and Adaptive Optics confocal Scanning Laser Ophthalmoscopy which have enabled single-cell visualisation in vivo. Cell size has previously been used to stratify Retinal Ganglion Cell (RGC) populations in histological samples of optic neuropathies, and early work in this field suggested that larger RGCs are more susceptible to early loss than smaller RGCs. More recently, however, it has been proposed that RGC soma and axon size may be dynamic and change in response to injury. To address this unresolved controversy, we applied recent advances in maximising information extraction from RGC populations in retinal whole mounts to evaluate the changes in RGC size distribution over time, using three well-established rodent models of optic nerve injury. In contrast to previous studies based on sampling approaches, we examined the whole Brn3a-positive RGC population at multiple time points over the natural history of these models. The morphology of over 4 million RGCs was thus assessed to glean novel insights from this dataset. RGC subpopulations were found to both increase and decrease in size over time, supporting the notion that RGC cell size is dynamic in response to injury. However, this study presents compelling evidence that smaller RGCs are lost more rapidly than larger RGCs despite the dynamism. Finally, using a bootstrap approach, the data strongly suggests that disease-associated changes in RGC spatial distribution and morphology could have potential as novel diagnostic indicators.
-
Conference paperLourenco A, Kerfoot E, Dibblin C, et al., 2020,
Automatic estimation of left atrial function from short axis CINE-MRI using machine learning
, European-Society-of-Cardiology (ESC) Congress, Publisher: OXFORD UNIV PRESS, Pages: 229-229, ISSN: 0195-668X- Author Web Link
- Cite
- Citations: 2
-
Journal articleHoward JP, Zaman S, Ragavan A, et al., 2020,
Automated analysis and detection of abnormalities in transaxial anatomical cardiovascular magnetic resonance images: a proof of concept study with potential to optimize image acquisition
, International Journal of Cardiovascular Imaging, Vol: 37, Pages: 1033-1042, ISSN: 1569-5794The large number of available MRI sequences means patients cannot realistically undergo them all, so the range of sequences to be acquired during a scan are protocolled based on clinical details. Adapting this to unexpected findings identified early on in the scan requires experience and vigilance. We investigated whether deep learning of the images acquired in the first few minutes of a scan could provide an automated early alert of abnormal features. Anatomy sequences from 375 CMR scans were used as a training set. From these, we annotated 1500 individual slices and used these to train a convolutional neural network to perform automatic segmentation of the cardiac chambers, great vessels and any pleural effusions. 200 scans were used as a testing set. The system then assembled a 3D model of the thorax from which it made clinical measurements to identify important abnormalities. The system was successful in segmenting the anatomy slices (Dice 0.910) and identified multiple features which may guide further image acquisition. Diagnostic accuracy was 90.5% and 85.5% for left and right ventricular dilatation, 85% for left ventricular hypertrophy and 94.4% for ascending aorta dilatation. The area under ROC curve for diagnosing pleural effusions was 0.91. We present proof-of-concept that a neural network can segment and derive accurate clinical measurements from a 3D model of the thorax made from transaxial anatomy images acquired in the first few minutes of a scan. This early information could lead to dynamic adaptive scanning protocols, and by focusing scanner time appropriately and prioritizing cases for supervision and early reporting, improve patient experience and efficiency.
-
Journal articleDai T, Liu H, Bharath A, 2020,
Episodic self-imitation learning with hindsight
, Electronics (Basel), Vol: 9, ISSN: 2079-9292Episodic self-imitation learning, a novel self-imitation algorithm with a trajectory selection module and an adaptive loss function, is proposed to speed up reinforcement learning. Compared to the original self-imitation learning algorithm, which samples good state–action pairs from the experience replay buffer, our agent leverages entire episodes with hindsight to aid self-imitation learning. A selection module is introduced to filter uninformative samples from each episode of the update. The proposed method overcomes the limitations of the standard self-imitation learning algorithm, a transitions-based method which performs poorly in handling continuous control environments with sparse rewards. From the experiments, episodic self-imitation learning is shown to perform better than baseline on-policy algorithms, achieving comparable performance to state-of-the-art off-policy algorithms in several simulated robot control tasks. The trajectory selection module is shown to prevent the agent learning undesirable hindsight experiences. With the capability of solving sparse reward problems in continuous control settings, episodic self-imitation learning has the potential to be applied to real-world problems that have continuous action spaces, such as robot guidance and manipulation.
-
Journal articleBrook J, Kim M-Y, Koutsoftidis S, et al., 2020,
Development of a pro-arrhythmic ex vivo intact human and porcine model: cardiac electrophysiological changes associated with cellular uncoupling
, Pflügers Archiv European Journal of Physiology, Vol: 472, Pages: 1435-1446, ISSN: 0031-6768We describe a human and large animal Langendorff experimental apparatus for live electrophysiological studies and measure the electrophysiological changes due to gap-junction uncoupling in human and porcine hearts. The resultant ex vivo intact human and porcine model can bridge the translational gap between smaller simple laboratory models and clinical research. In particular, electrophysiological models would benefit from the greater myocardial mass of a large heart due to its effects on far field signal, electrode contact issues and motion artefacts, consequently more closely mimicking the clinical setting Porcine (n=9) and human (n=4) donor hearts were perfused on a custom-designed Langendorff apparatus. Epicardial electrograms were collected at 16 sites across the left atrium and left ventricle. 1mM of carbenoxolone was administered at 5ml/min to induce cellular uncoupling, and then recordings were repeated at the same sites. Changes in electrogram characteristics were analysed.We demonstrate the viability of a controlled ex vivo model of intact porcine and human hearts for electrophysiology with pharmacological modulation. Carbenoxolone reduces cellular coupling and changes contact electrogram features. The time from stimulus artefact to (-dV/dt)max increased between baseline and carbenoxolone (47.9±4.1ms to 67.2±2.7ms) indicating conduction slowing. The features with the largest percentage change between baseline to Carbenoxolone were Fractionation +185.3%, Endpoint amplitude -106.9%, S-Endpoint Gradient +54.9%, S Point, -39.4%, RS Ratio +38.6% and (-dV/dt)max -20.9%.The physiological relevance of this methodological tool is that it provides a model to further investigate pharmacologically-induced proarrhythmic substrates.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.
Contact us
Prof Anil Anthony Bharath
a.bharath@imperial.ac.uk
Telephone
+44 (0)20 7594 5463
Address
Room 4.12
Department of Bioengineering
Royal School of Mines Building
Imperial College London