Implementation of medical imaging with telemedicine for the early detection and diagnoses of breast cancer to women in remote areas

Nowadays, the cancer topic has become a global concern. Furthermore, breast cancer persists to be the top leading cause of death to women population and the second cause of cancer death after the lung cancer globally. Various technologies and techniques have been searched, developed and studied over the years to detect the disease at the early stage; the early diagnosis saves many lives in both developed and developing countries. The detection of cancer through a screening process before its symptoms emerge increases the survival rate dramatically (Li, Meaney and Paulsen). Moreover, sufficient knowledge of the disease, qualified staff, accurate, appropriate treatment and diagnosis contribute to the successful cure of the disease; however, the cancer treatment is not affordable by many and sometimes not available to the very needy, and more precisely in developing countries. In this research, we aimed to explore the early detection of breast cancer using the new image compression algorithm: DYNAMAC, a compression tool that finds its basis in nonlinear dynamical systems theory; we implemented this algorithm through the D-transform, a digital sequence used to compress the digital media (Wang and Huang) & (Antoine, Murenzi and Vandergheynst). The goal is to use this method to analyze the average profile of diseased and healthy breast images obtained from a digital mammography to detect diseased tissues. After the detection of cancerous tumors, we worked to establish a remote care to women victims of breast cancer using the Telecommunication infrastructure through primarily Teleradiology and the Next Generation Internet (NGI) technology. Over the methods and techniques previously used in the area of medical imaging techniques, DYNAMAC algorithm is the most easily implemented along with its features that include cost saving in addition to best meeting the requirements of the breast imaging technology

Improving Workflow Efficiency for Mammography Using Machine Learning.

OBJECTIVE: The aim of this study was to determine whether machine learning could reduce the number of mammograms the radiologist must read by using a machine-learning classifier to correctly identify normal mammograms and to select the uncertain and abnormal examinations for radiological interpretation. METHODS: Mammograms in a research data set from over 7,000 women who were recalled for assessment at six UK National Health Service Breast Screening Program centers were used. A convolutional neural network in conjunction with multitask learning was used to extract imaging features from mammograms that mimic the radiological assessment provided by a radiologist, the patient's nonimaging features, and pathology outcomes. A deep neural network was then used to concatenate and fuse multiple mammogram views to predict both a diagnosis and a recommendation of whether or not additional radiological assessment was needed. RESULTS: Ten-fold cross-validation was used on 2,000 randomly selected patients from the data set; the remainder of the data set was used for convolutional neural network training. While maintaining an acceptable negative predictive value of 0.99, the proposed model was able to identify 34% (95% confidence interval, 25%-43%) and 91% (95% confidence interval: 88%-94%) of the negative mammograms for test sets with a cancer prevalence of 15% and 1%, respectively. CONCLUSION: Machine learning was leveraged to successfully reduce the number of normal mammograms that radiologists need to read without degrading diagnostic accuracy

Medical Devices Competitiveness and Impact on Public Health Expenditure

This study provides an analytical overview of the state of the European Union medical device industry. The medical device industry sector encompasses an extremely large variety of products and technologies. It covers hundreds of thousands of products that range from more traditional products, such as bandages or syringes, to sophisticated devices that incorporate bioinformatics, nanotechnology and engineered cells. These are designed for use by practitioners, patients and healthy individuals in a variety of settings: hospitals, surgeries and private homes. Besides being a vital and innovative industry, medical devices are a key component of healthcare systems and represent, together with pharmaceuticals, the bulk of ‘medical technology’. The analysis of the sector must therefore investigate medical devices as an industry – an innovative contributor to the economy – as well its key input to healthcare systems. The following aspects are taken into account: a) the impact of innovation in medical devices on health costs and expenditure; b) the innovativeness of the European medical device industry; c) the competitiveness of the European medical device industry as compared to that of the United States and Japan.healthcare expenditure; medical devices; competitiveness; innovation

Artificial Intelligence in Radiology

Since its first use in medical purpose in the 1960s, the concept of artificial intelligence has been especially appealing to health care, particularly radiology. With the development of ever more powerful computers from the 1990s to the present, various forms of artificial intelligence have found their way into different medical specialties – most notably radiology, dermatology, ophthalmology, and pathology. Due to the growing presence of such systems, it is paramount for the specialists handling them to get acquainted with them in order to provide the best service for their patients. It is therefore the aim of this article to explain the most basic principles of artificial intelligence, accentuating the most prominent concepts used in radiology today, such as deep learning and neural networks. It will also mention some of the artificial intelligence systems approved for clinical use in the US, such as IDx-DR, used to discover more than mild diabetic retinopathy in patients over 22 years of age; and Arterys, used for cardiac segmentation and discovering liver and lung nodules. Same as in many other fields, there is a constant need for improvement – in construction, testing, and application of these new technologies. Many ethical questions are asked, considering privacy and liability of artificial intelligence systems in clinical use. One of the greatest concerns for radiologists is the possibility of being replaced by these systems. This scenario seems to be far-fetched, at least for the time being. Radiologists should use that time to get to know the “enemy”. If they accomplish this, they might discover that they had had an ally all along

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

Phronesis of AI in radiology: Superhuman meets natural stupidity

Advances in AI in the last decade have clearly made economists, politicians, journalists, and citizenry in general believe that the machines are coming to take human jobs. We review 'superhuman' AI performance claims in radiology and then provide a self-reflection on our own work in the area in the form of a critical review, a tribute of sorts to McDermotts 1976 paper, asking the field for some self-discipline. Clearly there is an opportunity to replace humans, but there are better opportunities, as we have discovered to fit cognitive abilities of human and non-humans. We performed one of the first studies in radiology to see how human and AI performance can complement and improve each others performance for detecting pneumonia in chest X-rays. We question if there is a practical wisdom or phronesis that we need to demonstrate in AI today as well as in our field. Using this, we articulate what AI as a field has already and probably can in the future learn from Psychology, Cognitive Science, Sociology and Science and Technology Studies

Improvement of the portuguese breast cancer screening through process modelling (BPM)

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceBreast cancer is a malignant epithelial neoplasm with high incidence and mortality in women. Focusing the clinical performance on screening processes has proven to be the way to improve morbidity and mortality statistics of this recognized public health problem. Business process management (BPM) is a management field that improves and analyzes business processes according to organizations’ strategies. BPM may help manage patient and information flow, improving waiting time in healthcare delivery while integrating healthcare processes with IT. The early diagnosis of breast cancer is of great importance since it will enable more conservative treatments and a longer disease-free survival. Organized oncology screenings programs, with all elements properly prepared, revealed to be more efficient than the opportunistic screenings. The aim of this study is to identify and model BPM processes for the healthcare sector, namely, for the breast cancer screening in Portugal. To achieve this goal, the main processes were identified and new frameworks were proposed and validated through individual interviews with experts. In this study was concluded that BPM techniques can be applied to the healthcare. Through the application of these techniques it was possible to identify the main issues within the organized breast cancer screening and suggest changes to it. These changes focus on reducing the time of the process, improving its efficiency and offering greater support to the health user

Focal Spot, Winter 2007/2008

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