Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance in early chemotherapeutic stage from single cells in their native microenvironment, are still absen...
Research on quadrupedal robots inspired by canids or felids have been widely reported and demonstrated. However, none of these legged robots can deal with difficult environments that include water, such as small lakes, streams, rain, mud, flooded ter...
Hunting for chemicals with favorable pharmacological, toxicological, and pharmacokinetic properties remains a formidable challenge for drug discovery. Deep learning provides us with powerful tools to build predictive models that are appropriate for t...
BACKGROUND: Older individuals receiving home assistance are at high risk for emergency visits and unplanned hospitalization. Anticipating their health difficulties could prevent these events. This study investigated the effectiveness of an at-home mo...
BACKGROUND: Manual chart review is labor-intensive and requires specialized knowledge possessed by highly trained medical professionals. The cost and infrastructure challenges required to implement this is prohibitive for most hospitals. Natural lang...
BACKGROUND: Training therapists is both expensive and time-consuming. Degree-based training can require tens of thousands of dollars and hundreds of hours of expert instruction. Counseling skills practice often involves role-plays, standardized patie...
OBJECTIVES: To evaluate the diagnostic performance of deep learning with the convolutional neural networks (CNN) to distinguish each representative parkinsonian disorder using MRI.
Burns : journal of the International Society for Burn Injuries
Jun 21, 2019
BACKGROUND: Burn critical care represents a high impact population that may benefit from artificial intelligence and machine learning (ML). Acute kidney injury (AKI) recognition in burn patients could be enhanced by ML. The goal of this study was to ...
OBJECTIVES: To develop a proof-of-concept "interpretable" deep learning prototype that justifies aspects of its predictions from a pre-trained hepatic lesion classifier.
PURPOSE: To determine the feasibility of employing the prior knowledge of well-separated chemical exchange saturation transfer (CEST) signals in the 9.4 T Z-spectrum to separate overlapping CEST signals acquired at 3 T, using a deep learning approach...