Military medicine
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Using ultrasound to measure optic nerve sheath diameter (ONSD) has been shown to be a useful modality to detect elevated intracranial pressure. However, manual assessment of ONSD by a human operator is cumbersome and prone to human errors. We aimed to develop and test an automated algorithm for ONSD measurement using ultrasound images and compare it to measurements performed by physicians. ⋯ The automated image-analysis algorithm calculates ONSD reliably and with high precision when compared to measurements obtained by expert physicians. The algorithm may have a role in computer-aided decision support systems in acute brain injury.
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Early prediction of the acute hypotensive episode (AHE) in critically ill patients has the potential to improve outcomes. In this study, we apply different machine learning algorithms to the MIMIC III Physionet dataset, containing more than 60,000 real-world intensive care unit records, to test commonly used machine learning technologies and compare their performances. ⋯ We were able to predict AHE with precision and recall above 80% 30 minutes in advance with the large real-world dataset. The prediction of regression model can provide a more fine-grained, interpretable signal to practitioners. Model performance is improved by the inclusion of invasive features in predicting AHE, when compared to predicting the AHE based on only the available, restricted set of noninvasive technologies. This demonstrates the importance of exploring more noninvasive technologies for AHE prediction.
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Research shows that cognitive performance and emotional well-being can be significantly strengthened. A high-performance brain training protocol, Strategic Memory Advanced Reasoning Training (SMART), was developed by cognitive neuroscientists at The University of Texas at Dallas Center for BrainHealth based on 25-plus years of scientific study. Randomized controlled trials with various populations have shown that training and use of nine "SMART" strategies for processing information can improve cognitive performance and psychological health. However, the multi-week intensive training used in the laboratory is not practical for widespread use outside the laboratory. This article examines the efficacy of SMART when translated outside the laboratory to two populations (military/veterans and law enforcement) that received SMART in condensed time frames. ⋯ The results of translating to these two populations provide evidence supporting the efficacy of SMART delivered in an abbreviated time frame. The improvements in two major domains of cognitive function demonstrate that strategies can be taught and immediately applied by those receiving the training. The immediate psychological health improvements may be transient; however, the continued improvements in psychological health observed in a subset of the participants suggest that benefits may be sustainable even at later intervals.
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Scarcity of operating rooms and personal protective equipment in far-forward field settings make surgical infections a potential concern for combat mortality and morbidity. Surgical and transport personnel also face infectious risks from bodily fluid exposures. Our study aimed to describe the serial, proof-of-concept testing of the SurgiBox technology: an inflatable sterile environment that addresses the aforementioned problems, fits on gurneys and backpacks, and drapes over incisions. ⋯ Analytic, in silico, and mechanical airflow modeling and benchtop testing have helped to quantify the SurgiBox system's reliability in creating and maintaining an operating room-quality surgical field within the enclosure as well as protecting the surgical team outside the enclosure. More recent and ongoing work has focused on specifying optimal use settings in the casualty chain of care, expanding support for circumferential procedures, automating airflow control, and accelerating system setup. SurgiBox's ultimate goal is to take timely, safe surgery to patients in even the most austere of settings.
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Current thinking in healthcare education stipulates a holistic approach with a focus on patient management, bringing together technical skills, decision-making, and team performance. In parallel, training opportunities with actual patients have diminished, and the number of different interventions to master has increased. Training on simulators has become broadly accepted; however, requirements for such training devices have outpaced the development of new simulators. The Department of Defense (DoD) targeted this gap with a development challenge. This article introduces the Advanced Modular Manikin (AMM) platform and describes the path followed to address the challenge. ⋯ The formal release of a functional modular, interoperable open-source healthcare simulation platform is complete. Next steps involve a strategy for maintaining the open standards and verification of AMM-compatibility for modules. Increasing awareness of this powerful tool and prioritization of module-development to address the wide range of healthcare education needs could lead to a renaissance in military and civilian healthcare simulation-based training.