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

Neural Operator: Is data all you need to model the world? An insight into the impact of Physics Informed Machine Learning

Numerical approximations of partial differential equations (PDEs) are routinely employed to formulate the solution of physics, engineering and mathematical problems involving functions of several variables, such as the propagation of heat or sound, fluid flow, elasticity, electrostatics, electrodynamics, and more. While this has led to solving many complex phenomena, there are some limitations. Conventional approaches such as Finite Element Methods (FEMs) and Finite Differential Methods (FDMs) require considerable time and are computationally expensive. In contrast, data driven machine learning-based methods such as neural networks provide a faster, fairly accurate alternative, and have certain advantages such as discretization invariance and resolution invariance. This article aims to provide a comprehensive insight into how data-driven approaches can complement conventional techniques to solve engineering and physics problems, while also noting some of the major pitfalls of machine learning-based approaches. Furthermore, we highlight, a novel and fast machine learning-based approach (~1000x) to learning the solution operator of a PDE operator learning. We will note how these new computational approaches can bring immense advantages in tackling many problems in fundamental and applied physics

Cardiovascular Disorder Detection with a PSO-Optimized Bi-LSTM Recurrent Neural Network Model

The medical community is facing ever-increasing difficulties in identifying and treating cardiovascular diseases. The World Health Organization (WHO) reports that despite the availability of numerous high-priced medical remedies for persons with heart problems, CVDs continue to be the main cause of mortality globally, accounting for over 21 million deaths annually. When cardiovascular diseases are identified and treated early on, they cause far fewer deaths. Deep learning models have facilitated automated diagnostic methods for early detection of these diseases. Cardiovascular diseases often present insidious symptoms that are difficult to identify in a timely manner. Prompt diagnosis of individuals with CVD and related conditions, such as high blood pressure or high cholesterol, is crucial to initiate appropriate treatment. Recurrent neural networks (RNNs) with gated recurrent units (GRUs) have recently emerged as a more advanced variant, capable of surpassing Long Short-Term Memory (LSTM) models in several applications. When compared to LSTMs, GRUs have the advantages of faster calculation and less memory usage. When it comes to CVD prediction, the bio-inspired Particle Swarm Optimization (PSO) algorithm provides a straightforward method of getting the best possible outcomes with minimal effort. This stochastic optimization method requires neither the gradient nor any differentiated form of the objective function and emulates the behaviour and intelligence of swarms. PSO employs a swarm of agents, called particles, that navigate the search space to find the best prediction type.This study primarily focuses on predicting cardiovascular diseases using effective feature selection and classification methods. For CVD forecasting, we offer a GRU model built on recurrent neural networks and optimized with particle swarms (RNN-GRU-PSO). We find that the proposed model significantly outperforms the state-of-the-art models (98.2% accuracy in predicting cardiovascular diseases) in a head-to-head comparison

Graph Representation Forecasting of Patient's Medical Conditions: Toward a Digital Twin.

Objective: Modern medicine needs to shift from a wait and react, curative discipline to a preventative, interdisciplinary science aiming at providing personalized, systemic, and precise treatment plans to patients. To this purpose, we propose a "digital twin" of patients modeling the human body as a whole and providing a panoramic view over individuals' conditions. Methods: We propose a general framework that composes advanced artificial intelligence (AI) approaches and integrates mathematical modeling in order to provide a panoramic view over current and future pathophysiological conditions. Our modular architecture is based on a graph neural network (GNN) forecasting clinically relevant endpoints (such as blood pressure) and a generative adversarial network (GAN) providing a proof of concept of transcriptomic integrability. Results: We tested our digital twin model on two simulated clinical case studies combining information at organ, tissue, and cellular level. We provided a panoramic overview over current and future patient's conditions by monitoring and forecasting clinically relevant endpoints representing the evolution of patient's vital parameters using the GNN model. We showed how to use the GAN to generate multi-tissue expression data for blood and lung to find associations between cytokines conditioned on the expression of genes in the renin-angiotensin pathway. Our approach was to detect inflammatory cytokines, which are known to have effects on blood pressure and have previously been associated with SARS-CoV-2 infection (e.g., CXCR6, XCL1, and others). Significance: The graph representation of a computational patient has potential to solve important technological challenges in integrating multiscale computational modeling with AI. We believe that this work represents a step forward toward next-generation devices for precision and predictive medicine

Deep-learning based real-time prediction of acute kidney injury after cardiac surgery

The increasing digitisation of medical data and advances in artificial intelligence have enabled us to use the tremendous amount of data that is recorded during a hospital stay in a much more sophisticated way than is currently the case. In the study undertaken and published in the context of this doctoral project, this approach was taken for predicting postoperative acute kidney injury (AKI) – one of the most common and severe complications after cardiothoracic interventions. Using 96 parameters, standardly recorded during a hospital stay, a recurrent neural network (RNN) was developed that predicted AKI within the first seven postoperative days. The training of the model was based on n = 2224 admissions gathered from n = 15,564 admissions at a tertiary care hospital for cardiothoracic surgery. The performance of the model was assessed using an independent test set of n = 350 clinical cases and an area under the curve (AUC) (95% confidence interval) of 0.893 (0.862 - 0.924) was obtained. Additionally, a head-to-head comparison of the RNN against experienced physicians was conducted. The RNN exceeded the physicians in terms of all determined statistical measures (e.g., AUC = 0.901 vs 0.745, p < 0.001). In contrast to the predictions of physicians, who generally underrated the risk of developing AKI, the RNN showed good calibration. The integration of such a model into existing digital medical record systems could allow preventive steps to be taken in time to prevent complications by predicting AKI well before its onset. It could be used as a real-time surveillance system and support physicians' decision-making process. However, when using such a technique, there are several ethical aspects to be considered concerning data protection, model development, and clinical deployment, which are also discussed in this work.Die zunehmende Digitalisierung medizinischer Daten und die Fortschritte im Bereich der künstlichen Intelligenz ermöglichen es, die enorme Menge an Daten, die während eines Krankenhausaufenthalts gesammelt wird, auf viel komplexere Weise zu nutzen, als es bislang der Fall war. In der im Rahmen der Promotion durchgeführten Studie wurde dieser Ansatz für die Echtzeit-Vorhersage von postoperativem akutem Nierenversagen (ANV) verfolgt – eine der häufigsten Komplikationen nach kardiothorakalen Eingriffen. Anhand von 96 Parametern, die standardmäßig während eines Krankenhausaufenthalts aufgezeichnet werden, wurde ein rekurrentes neuronales Netz (RNN) entwickelt, das ANV innerhalb der ersten sieben postoperativen Tage vorhersagen kann. Das Modell wurde mit Daten aus n = 2224 Aufnahmen trainiert, welche aus n = 15.564 klinischen Fällen in einem Krankenhaus der tertiären Versorgung für kardiothorakale Chirurgie zusammengestellt wurden. Die Leistung des RNN wurde anhand eines unabhängigen Testsets aus n = 350 klinischen Fällen bewertet, und es wurde eine area under the curve (AUC) (95 % Konfidenzintervall) von 0,893 (0,862 - 0,924) ermittelt. Zusätzlich wurde ein direkter Vergleich der Vorhersagegüte zwischen dem RNN und erfahrenen ÄrztInnen durchgeführt. Das RNN übertraf die ÄrztInnen in Bezug auf alle ermittelten statistischen Messwerte (z.B. AUC = 0,901 vs. 0,745, p < 0,001). Im Gegensatz zu den Vorhersagen der ÄrztInnen, die das Risiko der Entwicklung eines ANV generell unterschätzten, zeigte das RNN eine gute Kalibrierung. Die Integration eines solchen Modells in bestehende elektronische Patientendatensysteme könnte durch frühzeitige Vorhersage von ANV ermöglichen, präventive Maßnahmen rechtzeitig zu ergreifen, um Komplikationen zu verhindern. Es könnte als Echtzeit-Überwachungssystem eingesetzt werden und die Entscheidungsprozesse der ÄrztInnen unterstützen. Bei der Verwendung eines solchen Systems sind neben seiner Vorhersagegüte aber auch ethische und rechtliche Aspekte zu berücksichtigen, die den Datenschutz, die Modellentwicklung und den klinischen Einsatz betreffen, und die in dieser Arbeit ebenfalls erörtert werden

Modeling The Spatiotemporal Dynamics Of Cells In The Lung

Multiple research problems related to the lung involve a need to take into account the spatiotemporal dynamics of the underlying component cells. Two such problems involve better understanding the nature of the allergic inflammatory response to explore what might cause chronic inflammatory diseases such as asthma, and determining the rules underlying stem cells used to engraft decellularized lung scaffolds in the hopes of growing new lungs for transplantation. For both problems, we model the systems computationally using agent-based modeling, a tool that enables us to capture these spatiotemporal dynamics by modeling any biological system as a collection of agents (cells) interacting with each other and within their environment. This allows to test the most important pieces of biological systems together rather than in isolation, and thus rapidly derive biological insights from resulting complex behavior that could not have been predicted beforehand, which we can then use to guide wet lab experimentation. For the allergic response, we hypothesized that stimulation of the allergic response with antigen results in a response with formal similarity to a muscle twitch or an action potential, with an inflammatory phase followed by a resolution phase that returns the system to baseline. We prepared an agent-based model (ABM) of the allergic inflammatory response and determined that antigen stimulation indeed results in a twitch-like response. To determine what might cause chronic inflammatory diseases where the twitch presumably cannot resolve back to baseline, we then tested multiple potential defects to the model. We observed that while most of these potential changes lessen the magnitude of the response but do not affect its overall behavior, extending the lifespan of activated pro-inflammatory cells such as neutrophils and eosinophil results in a prolonged inflammatory response that does not resolve to baseline. Finally, we performed a series of experiments involving continual antigen stimulation in mice, determining that there is evidence in the cytokine, cellular and physiologic (mechanical) response consistent with our hypothesis of a finite twitch and an associated refractory period. For stem cells, we made a 3-D ABM of a decellularized scaffold section seeded with a generic stem cell type. We then programmed in different sets of rules that could conceivably underlie the cell\u27s behavior, and observed the change in engraftment patterns in the scaffold over selected timepoints. We compared the change in those patterns against the change in experimental scaffold images seeded with C10 epithelial cells and mesenchymal stem cells, two cell types whose behaviors are not well understood, in order to determine which rulesets more closely match each cell type. Our model indicates that C10s are more likely to survive on regions of higher substrate while MSCs are more likely to proliferate on regions of higher substrate