Computer aided diagnosis of miliary TB in chest X-rays

Includes bibliography.With the improvement in computer technology, Computer Aided Diagnosis (CAD) is becoming an increasingly more powerful tool for radiologists. The focus of this project was on CAD of pulmonary miliary tuberculosis. Several methods for enhancing lung textures were discussed as an aid to the radiologist in diagnosing miliary TB. Some statistical approaches and template matching methods were used to measure characteristics of both healthy and unhealthy (miliary TB) lung textures. These measurements were evaluated to see if a computer can be programmed to differentiate between lung texture from a healthy lung and lung texture from a lung with miliary TB

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

Pattern recognition methods applied to medical imaging: lung nodule detection in computed tomography images

Lung cancer is one of the main public health issues in developed countries. The overall 5-year survival rate is only 10−16%, although the mortality rate among men in the United States has started to decrease by about 1.5% per year since 1991 and a similar trend for the male population has been observed in most European countries. By contrast, in the case of the female population, the survival rate is still decreasing, despite a decline in the mortality of young women has been ob- served over the last decade. Approximately 70% of lung cancers are diagnosed at too advanced stages for the treatments to be effective. The five-year survival rate for early-stage lung cancers (stage I), which can reach 70%, is sensibly higher than for cancers diagnosed at more advanced stages. Lung cancer most commonly manifests itself as non-calcified pulmonary nodules. The CT has been shown as the most sensitive imaging modality for the detection of small pulmonary nodules, particularly since the introduction of the multi-detector-row and helical CT technologies. Screening programs based on Low Dose Computed Tomography (LDCT) may be regarded as a promising technique for detecting small, early-stage lung cancers. The efficacy of screening programs based on CT in reducing the mortality rate for lung cancer has not been fully demonstrated yet, and different and opposing opinions are being pointed out on this topic by many experts. However, the recent results obtained by the National Lung Screening Trial (NLST), involving 53454 high risk patients, show a 20% reduction of mortality when the screening program was carried out with the helical CT, rather than with a conventional chest X-ray. LDCT settings are currently recommended by the screening trial protocols. However, it is not trivial in this case to identify small pulmonary nodules,due to the noisier appearance of the images in low-dose CT with respect to the standard-dose CT. Moreover, thin slices are generally used in screening programs, thus originating datasets of about 300 − 400 slices per study. De- pending on the screening trial protocol they joined, radiologists can be asked to identify even very small lung nodules, which is a very difficult and time- consuming task. Lung nodules are rather spherical objects, characterized by very low CT values and/or low contrast. Nodules may have CT values in the same range of those of blood vessels, airway walls, pleura and may be strongly connected to them. It has been demonstrated, that a large percent- age of nodules (20 − 35%) is actually missed in screening diagnoses. To support radiologists in the identification of early-stage pathological objects, about one decade ago, researchers started to develop CAD methods to be applied to CT examinations. Within this framework, two CAD sub-systems are proposed: CAD for internal nodules (CADI), devoted to the identification of small nodules embedded in the lung parenchyma, i.e. Internal Nodules (INs) and CADJP, devoted the identification of nodules originating on the pleura surface, i.e. Juxta-Pleural Nodules (JPNs) respectively. As the training and validation sets may drastically influence the performance of a CAD system, the presented approaches have been trained, developed and tested on different datasets of CT scans (Lung Image Database Consortium (LIDC), ITALUNG − CT) and finally blindly validated on the ANODE09 dataset. The two CAD sub-systems are implemented in the ITK framework, an open source C++ framework for segmentation and registration of medical im- ages, and the rendering of the obtained results are achieved using VTK, a freely available software system for 3D computer graphics, image processing and visualization. The Support Vector Machines (SVMs) are implemented in SVMLight. The two proposed approaches have been developed to detect solid nodules, since the number of Ground Glass Opacity (GGO) contained in the available datasets has been considered too low. This thesis is structured as follows: in the first chapter the basic concepts about CT and lung anatomy are explained. The second chapter deals with CAD systems and their evaluation methods. In the third chapter the datasets used for this work are described. In chapter 4 the lung segmentation algorithm is explained in details, and in chapter 5 and 6 the algorithms to detect internal and juxta-pleural candidates are discussed. In chapter 7 the reduction of false positives findings is explained. In chapter 8 results of the train and validation sessions are shown. Finally in the last chapter the conclusions are drawn

Enabling the Development and Implementation of Digital Twins : Proceedings of the 20th International Conference on Construction Applications of Virtual Reality

Welcome to the 20th International Conference on Construction Applications of Virtual Reality (CONVR 2020). This year we are meeting on-line due to the current Coronavirus pandemic. The overarching theme for CONVR2020 is "Enabling the development and implementation of Digital Twins". CONVR is one of the world-leading conferences in the areas of virtual reality, augmented reality and building information modelling. Each year, more than 100 participants from all around the globe meet to discuss and exchange the latest developments and applications of virtual technologies in the architectural, engineering, construction and operation industry (AECO). The conference is also known for having a unique blend of participants from both academia and industry. This year, with all the difficulties of replicating a real face to face meetings, we are carefully planning the conference to ensure that all participants have a perfect experience. We have a group of leading keynote speakers from industry and academia who are covering up to date hot topics and are enthusiastic and keen to share their knowledge with you. CONVR participants are very loyal to the conference and have attended most of the editions over the last eighteen editions. This year we are welcoming numerous first timers and we aim to help them make the most of the conference by introducing them to other participants

Current Perspective on the Study of Liquid-Fluid Interfaces: From Fundamentals to Innovative Applications

Fluid interfaces are promising candidates for confining different types of materials - e.g., polymers, surfactants, colloids, and even small molecules - and for designing new functional materials with reduced dimensionality. The development of such materials requires a deepening of the Physico-chemical bases underlying the formation of layers at fluid interfaces, as well as on the characterization of their structures and properties. This is of particular importance because the constraints associated with the assembly of materials at the interface lead to the emergence of equilibrium and dynamics features in the interfacial systems, which are far from those conventionally found in the traditional materials. This Special Issue is devoted to studies on fundamental and applied aspects of fluid interfaces, trying to provide a comprehensive perspective on the current status of the research field

Effect of a metacognitive intervention on cognitive heuristic use during diagnostic reasoning

Medical judgment and decision-making frequently occur under conditions of uncertainty. In order to reduce the complexity of diagnosis, physicians often rely on cognitive heuristics. Use of heuristics during clinical reasoning can be effective; however when used inappropriately the result can be flawed reasoning, medical errors and patient harm. Many researchers have attempted to debias individuals from inappropriate heuristic use by designing interventions based on normative theories of decision-making. There have been few attempts to debias individuals using interventions based on descriptive decision-making theories. Objectives: (1) Assess use of Anchoring and Adjustment and Confirmation Bias during diagnostic reasoning; (2) Investigate the impact of heuristic use on diagnostic accuracy; (3) Determine the impact of a metacognitive intervention based on the Mental Model Theory designed to reduce biased judgment by inducing physicians to 'think about how they think'; and (4) Test a novel technique using eye-tracking to determine heuristic use and diagnostic accuracy within mode of thinking as defined by the Dual Process Theory. Methods: Medical students and residents assessed clinical scenarios using a computer system, specified a diagnosis, and designated the data used to arrive at the diagnosis. During case analysis, subjects either verbalized their thoughts or wore eye-tracking equipment to capture eye movements and pupil size as they diagnosed cases. Diagnostic data specified by the subject was used to measure heuristic use and assess the impact of heuristic use on diagnostic accuracy. Eye-tracking data was used to determine the frequency of heuristic use (Confirmation Bias only) and mode of thinking. Statistic models were executed to determine the effect of the metacognitive intervention. Results: Use of cognitive heuristics during diagnostic reasoning was common for this subject population. Logistic regression showed case difficulty to be an important factor contributing to diagnostic error. The metacognitive intervention had no effect on heuristic use and diagnostic accuracy. Eye-tracking data reveal this subject population infrequently assess cases in the Intuitive mode of thinking; spend more time in the Analytical mode of thinking, and switches between the two modes frequently as they reason through a case to arrive at a diagnosis

Worker and Public Health and Safety

This book on "Worker and Public Health and Safety: Current Views" brings together current scholarly work and opinions in the form of original papers and reviews related to this field of study. It provides important and recent scientific reading as well as topical medical and occupational information and research in areas of immediate relevance, such as chronic and occupational diseases, worker safety and performance, job strain, workload, injuries, accident and errors, risks and management, fitness, burnout, psychological and mental disorders including stress, therapy, job satisfaction, musculoskeletal symptoms and pain, socio-economic factors, dust pollution, pesticides, noise, pathogens, and related areas