Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions

Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable quantification of atherosclerotic calcifications and thus enable identification of subjects at increased cardiovascular risk. This paper presents a method for automatic detection of coronary artery, thoracic aorta and cardiac valve calcifications in low-dose chest CT using two consecutive convolutional neural networks. The first network identifies and labels potential calcifications according to their anatomical location and the second network identifies true calcifications among the detected candidates. This method was trained and evaluated on a set of 1744 CT scans from the National Lung Screening Trial. To determine whether any reconstruction or only images reconstructed with soft tissue filters can be used for calcification detection, we evaluated the method on soft and medium/sharp filter reconstructions separately. On soft filter reconstructions, the method achieved F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta, aortic valve and mitral valve calcifications, respectively. On sharp filter reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively. Linearly weighted kappa coefficients for risk category assignment based on per subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter reconstructions, respectively. These results demonstrate that the presented method enables reliable automatic cardiovascular risk assessment in all low-dose chest CT scans acquired for lung cancer screening

The Role of Management Accounting and Control Systems as Information Networks and as Networks of Relationships on the Development of Organizational Knowledge

Management accounting and control systems (MACS) may be defined as part of the global information system, through which it is collected, processed, analyzed and communicated information (financial and nonfinancial, internal and external) used for planning, monitoring and control of different organizational activities, to optimize the use of resources, to support the process of decision making and to the performance evaluation process. In this way, the concept of MACS in use in this article expands the notion of management accounting systems to embrace the entire array of control mechanisms used by organizations (See Machintosh & Quattrone, 2010).The design of MACS as elements of organizational structure that favor the development of intellectual capital has been highlighted in the literature (Cf. e. g. Prieto, 1999; Tayles, Bramley, Adshead, & Farr, 2002; Roberts, 2003; Widener, 2004; Wingren, 2004; Bjurström & Roberts, 2007; Cleary, Kennedy, O’Donnell, O’Reagan, & Bontis, 2007; Tayles, Pike, & Sofian, 2007; Cleary, 2009; Massaro, Bardy, & Pitts, 2012; Massaro, Bardy, & Zanin, 2013), mainly as a means of communication, dissemination and interpretation favoring the interaction mechanisms essential to the creation, transmission and integration of organizational knowledge. In this article, MACS will be described as elements of the managerial apparatus that managers may use to promote the activities that invoke organizational knowledge, i.e., the fluid mix of framed experience, values, contextual information, and expert insight that provides a framework for evaluating and incorporating new experiences and information. Organizational knowledge often becomes embedded not only in documents or repositories but also in organizational routines, process, practices, and norms (Davenport & Prusak, 1998). The main objective of the article is to discuss the role of management accounting and control systems as information networks that collect, process and communicate information that influence the development of organizational knowledge, as well as networks of relationships that support the establishment of conditions for the creation and integration of organizational knowledge

Infective endocarditis caused by Arcanobacterium haemolyticum: a case report

Arcanobacterium haemolyticum is an organism that commonly causes pharyngitis and wound infections. It does not usually cause systemic invasive disease. The organism presents a difficult diagnostic problem because the Clinical Microbiology laboratory has a propensity to view them as diphtheroid organisms of the Corynebacterium species, thus contaminants or normal flora. We describe a case of a 21-year-old female who had endocarditis with cerebral emboli due to Arcanobacterium haemolyticum. This rare condition is associated with significant mortality and to the best of our knowledge; this is the first successfully treated case of A. haemolyticum endocarditis complicated by embolic phenomenon

UWOMJ Volume 49, No 1, March 1979

Schulich School of Medicine & Dentistryhttps://ir.lib.uwo.ca/uwomj/1034/thumbnail.jp

A Neutrosophic Clinical Decision-Making System for Cardiovascular Diseases Risk Analysis

Cardiovascular diseases are the leading cause of death worldwide. Early diagnosis of heart disease can reduce this large number of deaths so that treatment can be carried out. Many decision-making systems have been developed, but they are too complex for medical professionals. To target these objectives, we develop an explainable neutrosophic clinical decision-making system for the timely diagnose of cardiovascular disease risk. We make our system transparent and easy to understand with the help of explainable artificial intelligence techniques so that medical professionals can easily adopt this system. Our system is taking thirtyfive symptoms as input parameters, which are, gender, age, genetic disposition, smoking, blood pressure, cholesterol, diabetes, body mass index, depression, unhealthy diet, metabolic disorder, physical inactivity, pre-eclampsia, rheumatoid arthritis, coffee consumption, pregnancy, rubella, drugs, tobacco, alcohol, heart defect, previous surgery/injury, thyroid, sleep apnea, atrial fibrillation, heart history, infection, homocysteine level, pericardial cysts, marfan syndrome, syphilis, inflammation, clots, cancer, and electrolyte imbalance and finds out the risk of coronary artery disease, cardiomyopathy, congenital heart disease, heart attack, heart arrhythmia, peripheral artery disease, aortic disease, pericardial disease, deep vein thrombosis, heart valve disease, and heart failure. There are five main modules of the system, which are neutrosophication, knowledge base, inference engine, de-neutrosophication, and explainability. To demonstrate the complete working of our system, we design an algorithm and calculates its time complexity. We also present a new de-neutrosophication formula, and give comparison of our the results with existing methods

Analysis of Machine Learning Models for Heart Disease Prediction using Different Algorithms: A Review

Now a days the heart diseases are growing very rapidly making it an important and apprehensive task of prediction of these kinds of diseases in advance. The diagnosis is also a tough chore because it has to be performed in a precise and efficient manner. The emerging technology in modern life style integrated with internet of thing which having sensors and huge amount of data is sent to various clouds for further investigation using different algorithms to fetch out precise information for various domains. Across the world approximately 3 quintillion bytes/day information generated and this data stored for further examination. As data is in huge quantity therefore, appropriate methods applied to examine the perfect analysis so that prediction can be carried out optimally. Clinical decision making is dominant to all patient care happenings which includes choosing a deed, between replacements. These days emerging field like Machine Learning play prime role in healthcare to analyze and predict the diseases. After investigating numerous research article on Machine Learning, it was found that for same data set accuracy was different for various algorithms. In our research work different machine learning techniques will be implemented and will be tested for various parameters like accuracy, precision, recall on validated dataset. ML and Neural Networks are more capable in supporting deciding and predicting from the enormous data formed by health care systems