AIMC Topic: Cross-Sectional Studies
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Automated segmentation of an intensity calibration phantom in clinical CT images using a convolutional neural network.
International journal of computer assisted radiology and surgery
Mar 17, 2021
PURPOSE: In quantitative computed tomography (CT), manual selection of the intensity calibration phantom's region of interest is necessary for calculating density (mg/cm) from the radiodensity values (Hounsfield units: HU). However, as this manual pr...
Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients.
Sensors (Basel, Switzerland)
Mar 16, 2021
Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection and quantification of sarcopenia. A prototype deep learning neural network was trained on a multi-cen...
Parents' Perspectives on Using Artificial Intelligence to Reduce Technology Interference During Early Childhood: Cross-sectional Online Survey.
Journal of medical Internet research
Mar 15, 2021
BACKGROUND: Parents' use of mobile technologies may interfere with important parent-child interactions that are critical to healthy child development. This phenomenon is known as technoference. However, little is known about the population-wide aware...
Proposing a machine-learning based method to predict stillbirth before and during delivery and ranking the features: nationwide retrospective cross-sectional study.
BMC pregnancy and childbirth
Mar 12, 2021
BACKGROUND: Stillbirth is defined as fetal loss in pregnancy beyond 28 weeks by WHO. In this study, a machine-learning based method is proposed to predict stillbirth from livebirth and discriminate stillbirth before and during delivery and rank the f...
Convolutional neural network for classifying primary liver cancer based on triple-phase CT and tumor marker information: a pilot study.
Japanese journal of radiology
Mar 10, 2021
PURPOSE: To develop convolutional neural network (CNN) models for differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC) and predicting histopathological grade of HCC.
Machine learning models to identify low adherence to influenza vaccination among Korean adults with cardiovascular disease.
BMC cardiovascular disorders
Mar 9, 2021
BACKGROUND: Annual influenza vaccination is an important public health measure to prevent influenza infections and is strongly recommended for cardiovascular disease (CVD) patients, especially in the current coronavirus disease 2019 (COVID-19) pandem...
Robot-Based Assessment of HIV-Related Motor and Cognitive Impairment for Neurorehabilitation.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Mar 3, 2021
There is a pressing need for strategies to slow or treat the progression of functional decline in people living with HIV. This paper explores a novel rehabilitation robotics approach to measuring cognitive and motor impairment in adults living with H...
Three machine learning algorithms and their utility in exploring risk factors associated with primary cesarean section in low-risk women: A methods paper.
Research in nursing & health
Mar 2, 2021
Machine learning, a branch of artificial intelligence, is increasingly used in health research, including nursing and maternal outcomes research. Machine learning algorithms are complex and involve statistics and terminology that are not common in he...
Artificial intelligence in health data analysis: The Darwinian evolution theory suggests an extremely simple and zero-cost large-scale screening tool for prediabetes and type 2 diabetes.
Diabetes research and clinical practice
Feb 27, 2021
AIMS: The effective identification of individuals with early dysglycemia status is key to reduce the incidence of type 2 diabetes. We develop and validate a novel zero-cost tool that significantly simplifies the screening of undiagnosed dysglycemia.
Root causes of adverse drug events in hospitals and artificial intelligence capabilities for prevention.
Journal of advanced nursing
Feb 23, 2021
AIMS: To identify and prioritize the root causes of adverse drug events (ADEs) in hospitals and to assess the ability of artificial intelligence (AI) capabilities to prevent ADEs.
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