Invisible injuries: concussive effects and international humanitarian law

The concussive effects of weapons used on the modern battlefield can cause Traumatic Brain Injury (TBI). Indeed, TBI has been termed the "signature wound" of the ongoing conflicts in Iraq and Afghanistan. To date, the injury has not been taken into account by armed forces in their application of international humanitarian law norms regarding attacks that affect civilians. Of particular note in this regard are the rule of proportionality and the requirement to take precautions in attack. This article opens the discussion about this recently discovered consequence of warfare for the civilian population. It examines the state of the science regarding TBI and queries whether the understanding of such injuries has reached the point at which commanders in the field are obligated to begin considering, as a matter of humanitarian law, the risk of causing TBI to civilians when they attack enemy forces. It concludes with a practical assessment of how they might do so

Mechanisms and the Evidence Hierarchy

Evidence-based medicine (EBM) makes use of explicit procedures for grading evidence for causal claims. Normally, these procedures categorise evidence of correlation produced by statistical trials as better evidence for a causal claim than evidence of mechanisms produced by other methods. We argue, in contrast, that evidence of mechanisms needs to be viewed as complementary to, rather than inferior to, evidence of correlation. In this paper we first set out the case for treating evidence of mechanisms alongside evidence of correlation in explicit protocols for evaluating evidence. Next we provide case studies which exemplify the ways in which evidence of mechanisms complements evidence of correlation in practice. Finally, we put forward some general considerations as to how the two sorts of evidence can be more closely integrated by EBM

A Review of Causality for Learning Algorithms in Medical Image Analysis

Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease. Machine learning provides an additional boost for this area. However, machine learning for medical image analysis is particularly vulnerable to natural biases like domain shifts that affect algorithmic performance and robustness. In this paper we analyze machine learning for medical image analysis within the framework of Technology Readiness Levels and review how causal analysis methods can fill a gap when creating robust and adaptable medical image analysis algorithms. We review methods using causality in medical imaging AI/ML and find that causal analysis has the potential to mitigate critical problems for clinical translation but that uptake and clinical downstream research has been limited so far.Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://www.melba-journal.org/papers/2022:028.html". ; Paper ID: 2022:02

Jefferson Digital Commons quarterly report: October-December 2018

This quarterly report includes: Articles Dissertations From the Archives Grand Rounds and Lectures Industrial Design Capstones Journals and Newsletters LabArchives Launch Masters of Public Health Capstones Posters Reports Videos What People are Saying About the Jefferson Digital Common

Spring-damper equivalents of the fractional, poroelastic, and poroviscoelastic models for elastography

In MR elastography it is common to use an elastic model for the tissue's response in order to properly interpret the results. More complex models such as viscoelastic, fractional viscoelastic, poroelastic, or poroviscoelastic ones are also used. These models appear at first sight to be very different, but here it is shown that they all may be expressed in terms of elementary viscoelastic models. For a medium expressed with fractional models, many elementary spring-damper combinations are added, each of them weighted according to a long-tailed distribution, hinting at a fractional distribution of time constants or relaxation frequencies. This may open up for a more physical interpretation of the fractional models. The shear wave component of the poroelastic model is shown to be modeled exactly by a three-component Zener model. The extended poroviscoelastic model is found to be equivalent to what is called a non-standard four-parameter model. Accordingly, the large number of parameters in the porous models can be reduced to the same number as in their viscoelastic equivalents. As long as the individual displacements from the solid and fluid parts cannot be measured individually the main use of the poro(visco)elastic models is therefore as a physics based method for determining parameters in a viscoelastic model.Comment: 11 pages, 7 figures. Changed inconsistent notation in Eqs 1, 5, 8, 10 and corrected mistakes in Eqs 2, 4, 12, 3