Digital Twin of Cardiovascular Systems

Patient specific modelling using numerical methods is widely used in understanding diseases and disorders. It produces medical analysis based on the current state of patient’s health. Concurrently, as a parallel development, emerging data driven Artificial Intelligence (AI) has accelerated patient care. It provides medical analysis using algorithms that rely upon knowledge from larger human population data. AI systems are also known to have the capacity to provide a prognosis with overallaccuracy levels that are better than those provided by trained professionals. When these two independent and robust methods are combined, the concept of human digital twins arise. A Digital Twin is a digital replica of any given system or process. They combine knowledge from general data with subject oriented knowledge for past, current and future analyses and predictions. Assumptions made during numerical modelling are compensated using knowledge from general data. For humans, they can provide an accurate current diagnosis as well as possible future outcomes. This allows forprecautions to be taken so as to avoid further degradation of patient’s health.In this thesis, we explore primary forms of human digital twins for the cardiovascular system, that are capable of replicating various aspects of the cardiovascular system using different types of data. Since different types of medical data are available, such as images, videos and waveforms, and the kinds of analysis required may be offline or online in nature, digital twin systems should be uniquely designed to capture each type of data for different kinds of analysis. Therefore, passive, active and semi-active digital twins, as the three primary forms of digital twins, for different kinds of applications are proposed in this thesis. By the virtue of applications and the kind of data involved ineach of these applications, the performance and importance of human digital twins for the cardiovascular system are demonstrated. The idea behind these twins is to allow for the application of the digital twin concept for online analysis, offline analysis or a combination of the two in healthcare. In active digital twins active data, such as signals, is analysed online in real-time; in semi-active digital twin some of the components being analysed are active but the analysis itself is carried out offline; and finally, passive digital twins perform offline analysis of data that involves no active component.For passive digital twin, an automatic workflow to calculate Fractional Flow Reserve (FFR) is proposed and tested on a cohort of 25 patients with acceptable results. For semi-active digital twin, detection of carotid stenosis and its severity using face videos is proposed and tested with satisfactory results from one carotid stenosis patient and a small cohort of healthy adults. Finally, for the active digital twin, an enabling model is proposed using inverse analysis and its application in the detection of Abdominal Aortic Aneurysm (AAA) and its severity, with the help of a virtual patient database. This enabling model detected artificially generated AAA with an accuracy as high as 99.91% and classified its severity with acceptable accuracy of 97.79%. Further, for active digital twin, a truly active model is proposed for continuous cardiovascular state monitoring. It is tested on a small cohort of five patients from a publicly available database for three 10-minute periods, wherein this model satisfactorily replicated and forecasted patients’ cardiovascular state. In addition to the three forms of human digital twins for the cardiovascular system, an additional work on patient prioritisation in pneumonia patients for ITU care using data-driven digital twin is also proposed. The severity indices calculated by these models are assessed using the standard benchmark of Area Under Receiving Operating Characteristic Curve (AUROC). The results indicate that using these models, the ITU and mechanical ventilation can be prioritised correctly to an AUROC value as high as 0.89

An AI based digital-twin for prioritizing pneumonia patient treatment

A digital-twin based three-tiered system is proposed to prioritise patients for urgent intensive care and ventilator support. The deep learning methods are used to build patient-specific digital-twins to identify and prioritise critical cases amongst severe pneumonia patients. The three-tiered strategy is proposed to generate severity indices to: 1) identify urgent cases, 2) assign critical care and mechanical ventilation, and 3) discontinue mechanical ventilation and critical care at the optimal time. The severity indices calculated in the present study are the probability of death and the probability of requiring mechanical ventilation. These enable the generation of patient prioritisation lists and facilitates the smooth flow of patients in and out of Intensive Therapy Units (ITUs). The proposed digital-twin is built on pre-trained deep learning models using data from more than 1895 pneumonia patients. The severity indices calculated in the present study are assessed using the standard benchmark of Area Under Receiving Operating Characteristic Curve (AUROC). The results indicate that the ITU and mechanical ventilation can be prioritised correctly to an AUROC value as high as 0.89. This model may be employed in its current form to COVID-19 patients, but transfer learning with COVID-19 patient data will improve the predictions. The digital-twin model developed and tested is available via accompanyingsupplementary material

Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis

An exponential rise in patient data provides an excellent opportunity to improve the existing health care infrastructure. In the present work, a method to enable cardiovascular digital twin is proposed using inverse analysis. Conventionally, accurate analytical solutions for inverse analysis in linear problems have been proposed and used. However, these methods fail or are not efficient for nonlinear systems, such as blood flow in the cardiovascular system (systemic circulation) that involves high degree of nonlinearity. To address this, a methodology for inverse analysis using recurrent neural network for the cardiovascular system is proposed in this work, using a virtual patient database. Blood pressure waveforms in various vessels of the body are inversely calculated with the help of long short-term memory (LSTM) cells by inputting pressure waveforms from three non-invasively accessible blood vessels (carotid, femoral and brachial arteries). The inverse analysis system built this way is applied to the detection of abdominal aortic aneurysm (AAA) and its severity using neural networks

Artificial intelligence approaches to predict coronary stenosis severity using non-invasive fractional flow reserve

Fractional flow reserve is the current reference standard in the assessment of the functional impact of a stenosis in coronary heart disease. In this study, three models of artificial intelligence of varying degrees of complexity were compared to fractional flow reserve measurements. The three models are the multivariate polynomial regression, which is a statistical method used primarily for correlation; the feed-forward neural network; and the long short-term memory, which is a type of recurrent neural network that is suited to modelling sequences. The models were initially trained using a virtual patient database that was generated from a validated one-dimensional physics-based model. The feed-forward neural network performed the best for all test cases considered, which were a single vessel case from a virtual patient database, a multi-vessel network from a virtual patient database, and 25 clinically invasive fractional flow reserve measurements from real patients. The feed-forward neural network model achieved around 99% diagnostic accuracy in both tests involving virtual patients, and a respectable 72% diagnostic accuracy when compared to the invasive fractional flow reserve measurements. The multivariate polynomial regression model performed well in the single vessel case, but struggled on network cases as the variation of input features was much larger. The long short-term memory performed well for the single vessel cases, but tended to have a bias towards a positive fractional flow reserve prediction for the virtual multi-vessel case, and for the patient cases. Overall, the feed-forward neural network shows promise in successfully predicting fractional flow reserve in real patients, and could be a viable option if trained using a large enough data set of real patients

Data-driven inverse modelling through neural network (deep learning) and computational heat transfer

In this work, the potential of carrying out inverse problems with linear and non-linear behaviour is investigated using deep learning methods. In inverse problems, the boundary conditions are determined using sparse measurement of a variable such as velocity or temperature. Although this is mathematically tractable for simple problems, it can be extremely challenging for complex problems. To overcome the non-linear and complex effects, a brute force approach was used on a trial and error basis to find an approximate solution. With the advent of machine learning algorithms it may now be possible to model inverse problems faster and more accurately. In order to demonstrate that machine learning can be used in solving inverse problems, we propose a fusion between computational mechanics and machine learning. The forward problems are solved first to create a database. This database is then used to train the machine learning algorithms. The trained algorithm is then used to determine the boundary conditions of a problem from assumed measurements. The proposed method is tested for the linear/non-linear heat conduction, convection–conduction, and natural convection problems in which the boundary conditions are determined by providing three, four, and five temperature measurements. This study demonstrates that the proposed fusion of computational mechanics and machine learning is an effective way of tackling complex inverse problems

sj-zip-1-pih-10.1177_09544119221123431 – Supplemental material for An AI based digital-twin for prioritising pneumonia patient treatment

Supplemental material, sj-zip-1-pih-10.1177_09544119221123431 for An AI based digital-twin for prioritising pneumonia patient treatment by Neeraj Kavan Chakshu and Perumal Nithiarasu in Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine</p

Deep learning or interpolation for inverse modelling of heat and fluid flow problems?

PurposeThe purpose of this study is to compare interpolation algorithms and deep neural networks for inverse transfer problems with linear and nonlinear behaviour.Design/methodology/approachA series of runs were conducted for a canonical test problem. These were used as databases or “learning sets” for both interpolation algorithms and deep neural networks. A second set of runs was conducted to test the prediction accuracy of both approaches.FindingsThe results indicate that interpolation algorithms outperform deep neural networks in accuracy for linear heat conduction, while the reverse is true for nonlinear heat conduction problems. For heat convection problems, both methods offer similar levels of accuracy.Originality/valueThis is the first time such a comparison has been made

Automating fractional flow reserve (FFR) calculation from CT scans: A rapid workflow using unsupervised learning and computational fluid dynamics

Fractional flow reserve (FFR) provides the functional relevance of coronary atheroma. The FFR-guided strategy has been shown to reduce unnecessary stenting, improve overall health outcome, and to be cost-saving. The non-invasive, coronary Computerised Tomography (CT) angiography-derived FFR (cFFR) is an emerging method in reducing invasive catheter based measurements. This CFD-based method is laborious as it requires expertise in multidisciplinary analysis of combining image analysis and computational mechanics. In this work, we present a rapid method, powered by unsupervised learning, to automatically calculate cFFR from CT scans without manual intervention

Predicting the airborne microbial transmission via human breath particles using a gated recurrent units neural network

PurposeThe main purpose of this paper is to devise a tool, based on Computational Fluid Dynamics (CFD) and Machine Learning (ML), for the assessment of potential airborne microbial transmission in enclosed spaces. A Gated Recurrent Units Neural Network (GRU-NN) is presented to learn and predict the behaviour of droplets expelled through breaths via particle tracking datasets.Design/methodology/approachA computational methodology is used for investigating how infectious particles originated in one location are transported by air and spread throughout a room. High-fidelity prediction of indoor air flow is obtained by means of an in-house parallel CFD solver which employs a one equation Spalrat–Allmaras (SA) turbulence model. Several flow scenarios are considered by varying different ventilation conditions and source locations. The CFD model is used for computing the trajectories of the particles emitted human breath. The numerical results are used to the ML training.FindingIn this work, it is shown that the developed ML model, based on the Gated Recurrent Units Neural Network (GRU-NN), can accurately predict the airborne particle movement across an indoor environment for different vent operation conditions and source locations. The numerical results inthe paper prove that the presented methodology is able to provide accurate predictions of the time evolution of particle distribution at different locations of the enclosed space.Originality/valueThis study paves the way for the development of efficient and reliable tools for predicting virus airborne movement under different ventilation conditions and different human positions within an indoor environments, potentially leading to new design. A parametric study is carried out to evaluate the impact of system settings on the time variation particles emitted human breath within the space considered