Adaptive Modelling and Image-Based Monitoring for Artificially Ventilated Patients in the Intensive Care Unit (ICU)

The Intensive Care Unit (ICU) is where the critically-ill are treated. The first 24-hours (‘the golden hours’) of treatment is crucial to determine patient’s recovery and survival, and mechanical ventilation plays a major role as the main life support system in the ICU. The efficiency of mechanical ventilation and its management strategy are assessed by observing the arterial blood gases (ABG), which are sampled every few hours using a catheter inserted into the patient’s artery. This procedure is invasive thus can only be performed a handful of times each day. The ICU also has an abundance of underutilized data which until recently can only be translated by expert clinicians, who unfortunately always have clinical responsibilities to undertake concomitantly. This thesis proposes a series of new fuzzy logic-based models with a new type of fuzzy sets (type-2), which have not been investigated before in this clinical setting, for the relative dead-space (Kd), the carbon-dioxide production (VCO2), and the shunt sub-components for the SOPAVent (Sheffield Simulation of Patients under Artificial Ventilation) system, which performs predictions of arterial blood gases non-invasively and automatically. The Kd model, the VCO2 model and the resulting overall SOPAVent model are validated with retrospective real ICU patient data obtained from the Sheffield Royal Hallamshire Hospital (UK). The SOPAVent model is also validated with newly obtained data from patients diagnosed with Faecal Peritonitis (FP), from the Sheffield Royal Hallamshire Hospital (UK). Results showed an improved prediction accuracy for the Kd and the VCO2 sub-components when compared to existing systems. The prediction capability of SOPAVent is also improved from previous models for arterial blood gases before and after ventilator settings changes are made. A second new simplified model for predicting ABG using ventilator settings is also proposed with excellent prediction outcomes. Additionally, this thesis also looks into Electrical Impedance Tomography (EIT) as a potential bedside monitoring tool for pulmonary functions. EIT has the ability to provide a non-invasive, portable, and a relatively low cost alternative to other medical imaging systems. This thesis details the development of the hardware for a compact 16-electrode EIT measurement system, with the objective for future pulmonary applications. A method to generate three-dimensional (3D) images of the lungs from two-dimensional (2D) medical images of the thorax is also proposed with the estimation of lung volumes being presented

Biomedical Sensing and Imaging

This book mainly deals with recent advances in biomedical sensing and imaging. More recently, wearable/smart biosensors and devices, which facilitate diagnostics in a non-clinical setting, have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field. The aim of this book is to provide a research forum in biomedical sensing and imaging and extend the scientific frontier of this very important and significant biomedical endeavor

Multifrequency methods for Electrical Impedance Tomography

Multifrequency Electrical Impedance Tomography (MFEIT) is an emerging imaging modality which exploits the dependence of tissue impedance on frequency to recover images of conductivity. Given the low cost and portability of EIT scanners, MFEIT could provide emergency diagnosis of pathologies such as acute stroke, brain injury and breast cancer. Whereas time-difference, or dynamic, EIT is an established technique for monitoring lung ventilation, MFEIT has received less attention in the literature, and the imaging methodology is at an early stage of development. MFEIT holds the unique potential to form images from static data, but high sensitivity to noise and modelling errors must be overcome. The subject of this doctoral thesis is the investigation of novel techniques for including spectral information in the image reconstruction process. The aim is to improve the ill-posedness of the inverse problem and deliver the first imaging methodology with sufficient robustness for clinical application. First, a simple linear model for the conductivity is defined and a simultaneous multifrequency method is developed. Second, the method is applied to a realistic numerical model of a human head, and the robustness to modelling errors is investigated. Third, a combined image reconstruction and classification method is developed, which allows for the simultaneous recovery of the conductivity and the spectral information by introducing a Gaussian-mixture model for the conductivity. Finally, a graph-cut image segmentation technique is integrated in the imaging method. In conclusion, this work identifies spectral information as a key resource for producing MFEIT images and points to a new direction for the development of MFEIT algorithms

Deep neural network approach in human-like redundancy optimization for anthropomorphic manipulators

© 2013 IEEE. Human-like behavior has emerged in the robotics area for improving the quality of Human-Robot Interaction (HRI). For the human-like behavior imitation, the kinematic mapping between a human arm and robot manipulator is one of the popular solutions. To fulfill this requirement, a reconstruction method called swivel motion was adopted to achieve human-like imitation. This approach aims at modeling the regression relationship between robot pose and swivel motion angle. Then it reaches the human-like swivel motion using its redundant degrees of the manipulator. This characteristic holds for most of the redundant anthropomorphic robots. Although artificial neural network (ANN) based approaches show moderate robustness, the predictive performance is limited. In this paper, we propose a novel deep convolutional neural network (DCNN) structure for reconstruction enhancement and reducing online prediction time. Finally, we utilized the trained DCNN model for managing redundancy control a 7 DoFs anthropomorphic robot arm (LWR4+, KUKA, Germany) for validation. A demonstration is presented to show the human-like behavior on the anthropomorphic manipulator. The proposed approach can also be applied to control other anthropomorphic robot manipulators in industry area or biomedical engineering

Selected Papers from the 9th World Congress on Industrial Process Tomography

Industrial process tomography (IPT) is becoming an important tool for Industry 4.0. It consists of multidimensional sensor technologies and methods that aim to provide unparalleled internal information on industrial processes used in many sectors. This book showcases a selection of papers at the forefront of the latest developments in such technologies

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI