Development of Physics Applied to Medicine in the UK, 1945–90

Annotated and edited transcript of a Witness Seminar held on 5 July 2005. Introduction by Dr Jeff Hughes.First published by the Wellcome Trust Centre for the History of Medicine at UCL, 2006.©The Trustee of the Wellcome Trust, London, 2006.All volumes are freely available online at: www.history.qmul.ac.uk/research/modbiomed/wellcome_witnesses/Annotated and edited transcript of a Witness Seminar held on 5 July 2005. Introduction by Dr Jeff Hughes.Annotated and edited transcript of a Witness Seminar held on 5 July 2005. Introduction by Dr Jeff Hughes.Annotated and edited transcript of a Witness Seminar held on 5 July 2005. Introduction by Dr Jeff Hughes.Annotated and edited transcript of a Witness Seminar held on 5 July 2005. Introduction by Dr Jeff Hughes.Annotated and edited transcript of a Witness Seminar held on 5 July 2005. Introduction by Dr Jeff Hughes.Annotated and edited transcript of a Witness Seminar held on 5 July 2005. Introduction by Dr Jeff Hughes.Organized with the assistance of Professor John Clifton (UCL) and chaired by Professor Peter Williams (Manchester), this seminar examined the early developments of medical physics in the UK between 1945 and 1990. Participants discussed a range of themes including medical physics before and during the war, the role of the King's Fund and the formation of the Hospital Physicists' Association (HPA), expansion of medical physics outside radiotherapy and to non-radiation physics (ultrasound, medical instrumentation, bioengineering, use of digital computers), developing regional services and links with industry. The seminar finished with a discussion on the changing scene in the 1980s, covering topics such as funding, academic and undergraduate medical physics, imaging, CT, NMR and others. Participants included Mr Tom Ashton, Dr Barry Barber, Professors Roland Blackwell and Terence Burlin, Dr Joseph Blau, Mr Bob (John) Burns, Professors John Clifton, David Delpy, Philip Dendy and Jack Fowler, Dr Jean Guy, Mr John Haggith, Drs John Haybittle, Alan Jennings and John Law, Professors John Mallard and Joe McKie, Mr David Murnaghan, Professor Angela Newing, Dr Sydney Osborn, Professor Rodney Smallwood, Dr Adrian Thomas, Dr Peter Tothill, Mr Theodore Tulley, Professors Peter Wells and John West, and Mr John Wilkinson. Christie D A, Tansey E M. (eds) (2006) Development of physics applied to medicine in the UK, 1945–90, Wellcome Witnesses to Twentieth Century Medicine, vol. 28. London: The Wellcome Trust Centre for the History of Medicine at UCL.The Wellcome Trust Centre for the History of Medicine at UCL is funded by the Wellcome Trust, which is a registered charity, no. 210183

A Review of Atrial Fibrillation Detection Methods as a Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals

The Research Space: using the career paths of scholars to predict the evolution of the research output of individuals, institutions, and nations

In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps

Focal Spot, Fall 1985

https://digitalcommons.wustl.edu/focal_spot_archives/1041/thumbnail.jp

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

The future of laboratory medicine - A 2014 perspective.

Predicting the future is a difficult task. Not surprisingly, there are many examples and assumptions that have proved to be wrong. This review surveys the many predictions, beginning in 1887, about the future of laboratory medicine and its sub-specialties such as clinical chemistry and molecular pathology. It provides a commentary on the accuracy of the predictions and offers opinions on emerging technologies, economic factors and social developments that may play a role in shaping the future of laboratory medicine