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...
Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive e...
Artificial neural networks are machine-learning algorithms designed to analyse data without a pre-existing hypothesis as to any associations that may exist. This technique has not previously been applied to the risk stratification of patients referre...
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Jan 7, 2019
BACKGROUND: Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning ha...
Neurorehabilitation and neural repair
Dec 29, 2018
BACKGROUND: Passive robot-generated arm movements in conjunction with proprioceptive decision making and feedback modulate functional connectivity (FC) in sensory motor networks and improve sensorimotor adaptation in normal individuals. This proof-of...
AJR. American journal of roentgenology
Dec 12, 2018
OBJECTIVE: The purpose of this study is to determine whether a convolutional neural network (CNN) can predict the maximum standardized uptake value (SUV) of lymph nodes in patients with cancer using the unenhanced CT images from a PET/CT examination,...
AIMS: To evaluate if a deep learning algorithm can be trained to identify tumour-infiltrating lymphocytes (TILs) in tissue samples of testicular germ cell tumours and to assess whether the TIL counts correlate with relapse status of the patient.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Dec 5, 2018
Software testing of knowledge-based clinical decision support systems is challenging, labor intensive, and expensive; yet, testing is necessary since clinical applications have heightened consequences. Thoughtful test case selection improves testing ...
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