135 - Burkholderia (Glanders and Melioidosis) Attack

Burkholderia mallei and Burkholderia pseudomallei are the causative bacteria of the human diseases glanders and melioidosis, respectively. With high mortality and morbidity rates, significant antibiotic resistance, lack of a prophylactic vaccine, and highly pathogenic natures as aerosols, both are listed as Category B bioweapon agents by the Centers for Disease Control and Prevention (CDC) as well as Tier 1 overlap select agents by the CDC and the USDA. Given the innate low human transmissibility rate, the most logical and effective use of B. mallei or B. pseudomallei as a bioweapon agent would be dispersal as an aerosolized agent, which would markedly increase its infectious ability. This would imply that respiratory symptoms would predominate as a presenting complaint. If this were to occur in the middle of another respiratory outbreak (i.e., influenza or COVID-19), it would provide significant confounding and likely delay awareness that an attack has even occurred. Adding this to the unpredictable incubation period, variable presentations, and the possibility that many carriers will remain asymptomatic, it adds further difficulty to diagnosis until the organism is cultured in septic persons. Additionally, with diabetes and other immunocompromised human hosts growing more prevalent in the world population, there is escalating interest in these diseases as a public health issue, as both bacteria can cause opportunistic infections in these patient populations.

Anatomical Feature-Based Lung Ultrasound Image Quality Assessment Using Deep Convolutional Neural Network.

Lung ultrasound (LUS) has been used for point-of-care diagnosis of respiratory diseases including COVID-19, with advantages such as low cost, safety, absence of radiation, and portability. The scanning procedure and assessment of LUS are highly operator-dependent, and the appearance of LUS images varies with the probe's position, orientation, and contact force. Karamalis et al. introduced the concept of ultrasound confidence maps based on random walks to assess the ultrasound image quality algorithmically by estimating the per-pixel confidence in the image data. However, these confidence maps do not consider the clinical context of an image, such as anatomical feature visibility and diagnosability. This work proposes a deep convolutional network that detects important anatomical features in an LUS image to quantify its clinical context. This work introduces an Anatomical Feature-based Confidence (AFC) Map, quantifying an LUS image's clinical context based on the visible anatomical features. We developed two U-net models, each segmenting one of the two classes crucial for analyzing an LUS image, namely 1) Bright Features: Pleural and Rib Lines and 2) Dark Features: Rib Shadows. Each model takes the LUS image as input and outputs the segmented regions with confidence values for the corresponding class. The evaluation dataset consists of ultrasound images extracted from videos of two sub-regions of the chest above the anterior axial line from three human subjects. The feature segmentation models achieved an average Dice score of 0.72 on the model's output for the testing data. The average of non-zero confidence values in all the pixels was calculated and compared against the image quality scores. The confidence values were different between different image quality scores. The results demonstrated the relevance of using an AFC Map to quantify the clinical context of an LUS image.

Tele-Operative Low-Cost Robotic Lung Ultrasound Scanning Platform for Triage of COVID-19 Patients.

Novel severe acute respiratory syndrome coronavirus 2 (COVID-19) has become a pandemic of epic proportions, and global response to prepare health systems worldwide is of utmost importance. 2-dimensional (2D) lung ultrasound (LUS) has emerged as a rapid, noninvasive imaging tool for diagnosing COVID-19 infected patients. Concerns surrounding LUS include the disparity of infected patients and healthcare providers, and importantly, the requirement for substantial physical contact between the patient and operator, increasing the risk of transmission. New variants of COVID-19 will continue to emerge; therefore, mitigation of the virus's spread is of paramount importance. A tele-operative robotic ultrasound platform capable of performing LUS in COVID-19 infected patients may be of significant benefit, especially in low- and middle-income countries. The authors address the issues mentioned above surrounding the use of LUS in COVID-19 infected patients and the potential for extension of this technology in a resource-limited environment. Additionally, first-time application, feasibility, and safety were validated in healthy subjects. Preliminary results demonstrate that our platform allows for the successful acquisition and application of robotic LUS in humans.