Bevel-Tip Needle Deflection Modeling, Simulation, and Validation in Multi-Layer Tissues.

Percutaneous needle insertions are commonly performed for diagnostic and therapeutic purposes as an effective alternative to more invasive surgical procedures. However, the outcome of needle-based approaches relies heavily on the accuracy of needle placement, which remains a challenge even with robot assistance and medical imaging guidance due to needle deflection caused by contact with soft tissues. In this paper, we present a novel mechanics-based 2D bevel-tip needle model that can account for the effect of nonlinear strain-dependent behavior of biological soft tissues under compression.

Application of noise-reduction techniques to machine learning algorithms for breast cancer tumor identification.

The application of machine learning (ML) techniques to digitized images of biopsied cells for breast cancer diagnosis is an active area of research. We hypothesized that reducing noise in the data would lead to an increase in classification accuracies. To test this hypothesis, we first compared several classification techniques in their ability to discriminate between malignant and benign breast cancer tumors using the Wisconsin Breast Cancer Data Set and subsequently evaluated the effect of noise reduction techniques on model accuracies.

Autonomous Robotic Point-of-Care Ultrasound Imaging for Monitoring of COVID-19-Induced Pulmonary Diseases.

The COVID-19 pandemic has emerged as a serious global health crisis, with the predominant morbidity and mortality linked to pulmonary involvement. Point-of-Care ultrasound (POCUS) scanning, becoming one of the primary determinative methods for its diagnosis and staging, requires, however, close contact of healthcare workers with patients, therefore increasing the risk of infection. This work thus proposes an autonomous robotic solution that enables POCUS scanning of COVID-19 patients' lungs for diagnosis and staging.

A 3-D-Printed Patient-Specific Ultrasound Phantom for FAST Scan.

Ultrasound phantoms are commonly used to assess the performance of ultrasound systems and ensure their proper functionality, in addition to providing opportunities for medical training. However, Focused Assessment with Sonography for Trauma (FAST) phantoms, in particular, are prohibitively expensive and procedure specific. This work explores the use of additive manufacturing to fabricate a patient-specific, full-scale torso ultrasound phantom.