Computer-aided interpretation of medical images: Mammography case study, Machine Graphics & Vision 16(3/4): 347–375

Abstract. This paper presents the current limitations and challenges of computer-aided interpretation of radiological examinations. The analysis and the proposed improvements of interpretation arose from our experience, knowledge and observations with the collected suggestions and conclusions. The emphasized topics are as follows: computer understanding of human determinants of diagnosis, characteristics and enhancement of observer performance, diagnostic accuracy measures of image examinations, computer-aided diagnosis (CAD) systems, and numerical description of medical image-based content. All of these diagnosis support concepts can be integrated into an intelligent diagnosis interface and enhanced, basing on a formal description of semantic image content, i.e. ontology implied as a reliable, dynamic platform of medical knowledge, useful for diagnosis. CAD for mammography and content-based image indexing supported by the ontology were integrated for the needs of enhanced diagnostic workstation applied in tele-information medical systems. A design of an effective human-machine interface has arisen as the leading problem of the current challenges

Potential Preventive Strategies for Amyotrophic Lateral Sclerosis

It may seem useless to propose preventive measures for a disease without established pathogenesis and successful therapy, such as amyotrophic lateral sclerosis (ALS). However, we will show that ALS shares essential molecular mechanisms with aging and that established anti-aging strategies, such as healthy diet or individually adjusted exercise, may be successfully applied to ameliorate the condition of ALS patients. These strategies might be applied for prevention if persons at ALS risk could be identified early enough. Recent research advances indicate that this may happen soon