A Review on the use of Artificial Intelligence Techniques in Brain MRI Analysis

Over the past 20 years, the global research going on in Artificial Intelligence in applica-tions in medication is a venue internationally, for medical trade and creating an ener-getic research community. The Artificial Intelligence in Medicine magazine has posted a massive amount. This paper gives an overview of the history of AI applications in brain MRI analysis to research its effect at the wider studies discipline and perceive de-manding situations for its destiny. Analysis of numerous articles to create a taxono-my of research subject matters and results was done. The article is classed which might be posted between 2000 and 2018 with this taxonomy. Analyzed articles have excessive citations. Efforts are useful in figuring out popular studies works in AI primarily based on mind MRI analysis throughout specific issues. The biomedical prognosis was ruled by way of knowledge engineering research in its first decade, whilst gadget mastering, and records mining prevailed thereafter. Together these two topics have contributed a lot to the latest medical domain

Measurements, Models, Systems and Design

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Extraction of Heart Rate from Multimodal Video Streams of Neonates using Methods of Machine Learning

The World Health Organization estimates that more than one-tenth of births are premature. Premature births are linked to an increase of the mortality risk, when compared with full-term infants. In fact, preterm birth complications are the leading cause of perinatal mortality. These complications range from respiratory distress to cardiovascular disorders. Vital signs changes are often prior to these major complications, therefore it is crucial to perform continuous monitoring of this signals. Heart rate monitoring is particularly important. Nowadays, the standard method to monitor this vital sign requires adhesive electrodes or sensors that are attached to the infant. This contact-based methods can damage the skin of the infant, possibly leading to infections. Within this context, there is a need to evolve to remote heart rate monitoring methods. This thesis introduces a new method for region of interest selection to improve remote heart rate monitoring in neonatology through Photoplethysmography Imaging. The heart rate assessment is based on the standard photoplethysmography principle, which makes use of the subtle fluctuations of visible or infrared light that is reflected from the skin surface within the cardiac cycle. A camera is used, instead of the contact-based sensors. Specifically, this thesis presents an alternative method to manual region of interest selection using methods of Machine Learning, aiming to improve the robustness of Photoplethysmography Imaging. This method comprises a highly efficient Fully Convolutional Neural Network to select six different body regions, within each video frame. The developed neural network was built upon a ResNet network and a custom upsampling network. Additionally, a new post-processing method was developed to refine the body segmentation results, using a sequence of morphological operations and centre of mass analysis. The developed region of interest selection method was validated with clinical data, demonstrating a good agreement (78%) between the estimated heart rate and the reference

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

Defect detection in infrared thermography by deep learning algorithms

L'évaluation non destructive (END) est un domaine permettant d'identifier tous les types de dommages structurels dans un objet d'intérêt sans appliquer de dommages et de modifications permanents. Ce domaine fait l'objet de recherches intensives depuis de nombreuses années. La thermographie infrarouge (IR) est l'une des technologies d'évaluation non destructive qui permet d'inspecter, de caractériser et d'analyser les défauts sur la base d'images infrarouges (séquences) provenant de l'enregistrement de l'émission et de la réflexion de la lumière infrarouge afin d'évaluer les objets non autochauffants pour le contrôle de la qualité et l'assurance de la sécurité. Ces dernières années, le domaine de l'apprentissage profond de l'intelligence artificielle a fait des progrès remarquables dans les applications de traitement d'images. Ce domaine a montré sa capacité à surmonter la plupart des inconvénients des autres approches existantes auparavant dans un grand nombre d'applications. Cependant, en raison de l'insuffisance des données d'entraînement, les algorithmes d'apprentissage profond restent encore inexplorés, et seules quelques publications font état de leur application à l'évaluation non destructive de la thermographie (TNDE). Les algorithmes d'apprentissage profond intelligents et hautement automatisés pourraient être couplés à la thermographie infrarouge pour identifier les défauts (dommages) dans les composites, l'acier, etc. avec une confiance et une précision élevée. Parmi les sujets du domaine de recherche TNDE, les techniques d'apprentissage automatique supervisées et non supervisées sont les tâches les plus innovantes et les plus difficiles pour l'analyse de la détection des défauts. Dans ce projet, nous construisons des cadres intégrés pour le traitement des données brutes de la thermographie infrarouge à l'aide d'algorithmes d'apprentissage profond et les points forts des méthodologies proposées sont les suivants: 1. Identification et segmentation automatique des défauts par des algorithmes d'apprentissage profond en thermographie infrarouge. Les réseaux neuronaux convolutifs (CNN) pré-entraînés sont introduits pour capturer les caractéristiques des défauts dans les images thermiques infrarouges afin de mettre en œuvre des modèles basés sur les CNN pour la détection des défauts structurels dans les échantillons composés de matériaux composites (diagnostic des défauts). Plusieurs alternatives de CNNs profonds pour la détection de défauts dans la thermographie infrarouge. Les comparaisons de performance de la détection et de la segmentation automatique des défauts dans la thermographie infrarouge en utilisant différentes méthodes de détection par apprentissage profond : (i) segmentation d'instance (Center-mask ; Mask-RCNN) ; (ii) détection d’objet (Yolo-v3 ; Faster-RCNN) ; (iii) segmentation sémantique (Unet ; Res-unet); 2. Technique d'augmentation des données par la génération de données synthétiques pour réduire le coût des dépenses élevées associées à la collecte de données infrarouges originales dans les composites (composants d'aéronefs.) afin d'enrichir les données de formation pour l'apprentissage des caractéristiques dans TNDE; 3. Le réseau antagoniste génératif (GAN convolutif profond et GAN de Wasserstein) est introduit dans la thermographie infrarouge associée à la thermographie partielle des moindres carrés (PLST) (réseau PLS-GANs) pour l'extraction des caractéristiques visibles des défauts et l'amélioration de la visibilité des défauts pour éliminer le bruit dans la thermographie pulsée; 4. Estimation automatique de la profondeur des défauts (question de la caractérisation) à partir de données infrarouges simulées en utilisant un réseau neuronal récurrent simplifié : Gate Recurrent Unit (GRU) à travers l'apprentissage supervisé par régression.Non-destructive evaluation (NDE) is a field to identify all types of structural damage in an object of interest without applying any permanent damage and modification. This field has been intensively investigated for many years. The infrared thermography (IR) is one of NDE technology through inspecting, characterize and analyzing defects based on the infrared images (sequences) from the recordation of infrared light emission and reflection to evaluate non-self-heating objects for quality control and safety assurance. In recent years, the deep learning field of artificial intelligence has made remarkable progress in image processing applications. This field has shown its ability to overcome most of the disadvantages in other approaches existing previously in a great number of applications. Whereas due to the insufficient training data, deep learning algorithms still remain unexplored, and only few publications involving the application of it for thermography nondestructive evaluation (TNDE). The intelligent and highly automated deep learning algorithms could be coupled with infrared thermography to identify the defect (damages) in composites, steel, etc. with high confidence and accuracy. Among the topics in the TNDE research field, the supervised and unsupervised machine learning techniques both are the most innovative and challenging tasks for defect detection analysis. In this project, we construct integrated frameworks for processing raw data from infrared thermography using deep learning algorithms and highlight of the methodologies proposed include the following: 1. Automatic defect identification and segmentation by deep learning algorithms in infrared thermography. The pre-trained convolutional neural networks (CNNs) are introduced to capture defect feature in infrared thermal images to implement CNNs based models for the detection of structural defects in samples made of composite materials (fault diagnosis). Several alternatives of deep CNNs for the detection of defects in the Infrared thermography. The comparisons of performance of the automatic defect detection and segmentation in infrared thermography using different deep learning detection methods: (i) instance segmentation (Center-mask; Mask-RCNN); (ii) objective location (Yolo-v3; Faster-RCNN); (iii) semantic segmentation (Unet; Res-unet); 2. Data augmentation technique through synthetic data generation to reduce the cost of high expense associated with the collection of original infrared data in the composites (aircraft components.) to enrich training data for feature learning in TNDE; 3. The generative adversarial network (Deep convolutional GAN and Wasserstein GAN) is introduced to the infrared thermography associated with partial least square thermography (PLST) (PLS-GANs network) for visible feature extraction of defects and enhancement of the visibility of defects to remove noise in Pulsed thermography; 4. Automatic defect depth estimation (Characterization issue) from simulated infrared data using a simplified recurrent neural network: Gate Recurrent Unit (GRU) through the regression supervised learning

Automated analysis of heterogeneous catalyst materials using deep learning

Heterogeneous catalyst materials play a key role in modern society, as many processes in the chemical and energy industry rely on them. Optimising their performance is directly connected to a large potential of reductions in energy consumption, and thus to a more sustainable future. A fundamental part in the optimisation process is represented by materials characterisation. This is often done using in situ (Scanning) Transmission Electron Microscopy ((S)TEM) imaging in order to obtain a full understanding of the catalyst performance in different environments and temperatures at high resolution. However, the analysis of corresponding dynamical datasets is often time-consuming and requires manual intervention alongside tailored post-processing routines. At the same time, the emergence of direct electron detectors allowing for the acquisition of datasets at kiloHertz frame rates, as well as novel imaging techniques raised data generation rates significantly and created a need for new, reliable and automated data processing techniques. This work introduces nNPipe as Deep Learning based method for the automated analysis of morphologically diverse heterogeneous catalyst systems. The method is based on two Covolutional Neural Networks (CNNs) that were exclusively trained on computationally generated HRTEM image simulations and allow for rapid and precise analysis of raw 2048 x 2048 experimental HRTEM images. The performance of nNPipe is demonstrated in a realistic automated imaging scenario where statistically significant material properties are inferred accurately. Moreover, time-efficient and reproducible retraining methods based small experimental datasets are described for both, further performance improvements and adaption to new imaging scenarios. In this context, a potentially new pathway for the generation of suitable training datasets obtained by thousands of mostly non-expert annotations is highlighted. Finally, the analytical capacities of the nNPipe method are showcased on time-resolved datasets in two advanced applications scenarios: i) Live image analysis during sample acquisition and ii) Analysis of the particle coalescence of an in situ heated Pd/C catalyst