Improving Classification of Tetanus Severity for Patients in Low-Middle Income Countries Wearing ECG Sensors by Using a CNN-Transformer Network.

Tetanus is a life-threatening infectious disease, which is still common in low- and middle-income countries, including in Vietnam. This disease is characterized by muscle spasm and in severe cases is complicated by autonomic dysfunction. Ideally continuous vital sign monitoring using bedside monitors allows the prompt detection of the onset of autonomic nervous system dysfunction or avoiding rapid deterioration.

Landmark tracking in 4D ultrasound using generalized representation learning.

Purpose: In this study, we present and validate a novel concept for target tracking in 4D ultrasound. The key idea is to replace image patch similarity metrics by distances in a latent representation. For this, 3D ultrasound patches are mapped into a representation space using sliced-Wasserstein autoencoders.

Classification of Tetanus Severity in Intensive-Care Settings for Low-Income Countries Using Wearable Sensing.

Infectious diseases remain a common problem in low- and middle-income countries, including in Vietnam. Tetanus is a severe infectious disease characterized by muscle spasms and complicated by autonomic nervous system dysfunction in severe cases. Patients require careful monitoring using electrocardiograms (ECGs) to detect deterioration and the onset of autonomic nervous system dysfunction as early as possible.

Sepsis Mortality Prediction Using Wearable Monitoring in Low-Middle Income Countries.

Sepsis is associated with high mortality-particularly in low-middle income countries (LMICs). Critical care management of sepsis is challenging in LMICs due to the lack of care providers and the high cost of bedside monitors. Recent advances in wearable sensor technology and machine learning (ML) models in healthcare promise to deliver new ways of digital monitoring integrated with automated decision systems to reduce the mortality risk in sepsis.

Using Deep Neural Networks to Improve Contact Wrench Estimation of Serial Robotic Manipulators in Static Tasks.

Reliable force-driven robot-interaction requires precise contact wrench measurements. In most robot systems these measurements are severely incorrect and in most manipulation tasks expensive additional force sensors are installed. We follow a learning approach to train the dependencies between joint torques and end-effector contact wrenches.

Aortic Valve Leaflet Shape Synthesis With Geometric Prior From Surrounding Tissue.

Even though the field of medical imaging advances, there are structures in the human body that are barely assessible with classical image acquisition modalities. One example are the three leaflets of the aortic valve due to their thin structure and high movement. However, with an increasing accuracy of biomechanical simulation, for example of the heart function, and extense computing capabilities available, concise knowledge of the individual morphology of these structures could have a high impact on personalized therapy and intervention planning as well as on clinical research.

Medical Robotics for Ultrasound Imaging: Current Systems and Future Trends.

Purpose Of Review: This review provides an overview of the most recent robotic ultrasound systems that have contemporary emerged over the past five years, highlighting their status and future directions. The systems are categorized based on their level of robot autonomy (LORA).

Recent Findings: Teleoperating systems show the highest level of technical maturity.