Point-of-care ultrasound (PoCUS) is used by health care professionals of various specialties worldwide, with excellent results demonstrating significant potential to advance patient care. However, in low resource areas of the world, where other imaging modalities are scarce and the potential of handheld pocket-sized PoCUS devices with great versatility and increasing affordability seems most significant, its use is far from being widespread. In this report, our group of Chadian, Israeli, and Canadian physicians with experience in rural, military, and conflict zone medical aid, discusses the barriers to the implementation of PoCUS in low resource areas and offers potential solutions.

Where do armed conflicts occur? In applied studies, we may take ad hoc approaches to answer this question. In some regression studies, for instance, a single conflict event can cause an entire province to be classified as a conflict zone. In this paper, I fill this void of knowledge by developing a machine learning method that is less dependent on the areal-unit assumptions and can flexibly estimate conflict zones. I apply the method to a conflict event dataset and create a new dataset of conflict zones. A replication of Daskin and Pringle (2018, Nature 553, 328–332) with the new dataset indicates that the effect of civil war on mammal populations is much smaller than the original estimate.