AIMC Topic: Reproducibility of Results

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Translation into portuguese of a set of questionnaires designed to evaluate the impact of using a telepresence robot during postoperative ward rounds.

Revista do Colegio Brasileiro de Cirurgioes
INTRODUCTION: the use of telepresence grows with the advancement of technology integration into medical practice. Regarding surgery, effective distance communication can translate into better perioperative care. Though, the patients' perception about...

Deep learning-based neural networks for day-ahead power load probability density forecasting.

Environmental science and pollution research international
Energy efficiency is crucial to greenhouse gas (GHG) emission pathways reported by the Intergovernmental Panel on Climate Change. Electrical overload frequently occurs and causes unwanted outages in distribution networks, which reduces energy utiliza...

Analysis of Security Issues in Wireless Body Area Networks in Heterogeneous Networks.

Sensors (Basel, Switzerland)
Body Area Network (BAN) is one of the most important techniques for observing patient health in real time and identifying and analyzing diseases. For effective implementation of this technology in practice and to benefit from it, there are some key i...

EnsembleSplice: ensemble deep learning model for splice site prediction.

BMC bioinformatics
BACKGROUND: Identifying splice site regions is an important step in the genomic DNA sequencing pipelines of biomedical and pharmaceutical research. Within this research purview, efficient and accurate splice site detection is highly desirable, and a ...

Evaluation of two semi-supervised learning methods and their combination for automatic classification of bone marrow cells.

Scientific reports
Differential bone marrow (BM) cell counting is an important test for the diagnosis of various hematological diseases. However, it is difficult to accurately classify BM cells due to non-uniformity and the lack of reproducibility of differential count...

A deep learning approach to real-time volumetric measurements without image reconstruction for cardiovascular magnetic resonance.

Physiological measurement
Cardiovascular magnetic resonance (CMR) can measure ventricular volumes for the quantitative assessment of cardiac function in clinical cardiology. Conventionally, CMR volumetric measurements require image reconstruction and segmentation. There are l...

Influence of Voice Interactive Educational Robot Combined with Artificial Intelligence for the Development of Adolescents.

Computational intelligence and neuroscience
In the context of multicultural information, to explore and analyze the use effect of voice interactive educational robot in the classroom of adolescent students, and the physical and mental impact of movie characters on adolescent students, and to l...

A Hybrid Catheter Localisation Framework in Echocardiography Based on Electromagnetic Tracking and Deep Learning Segmentation.

Computational intelligence and neuroscience
Interventional cardiology procedure is an important type of minimally invasive surgery that deals with the catheter-based treatment of cardiovascular diseases, such as coronary artery diseases, strokes, peripheral arterial diseases, and aortic diseas...

Accelerated 4D-flow MRI with 3-point encoding enabled by machine learning.

Magnetic resonance in medicine
PURPOSE: To investigate the acceleration of 4D-flow MRI using a convolutional neural network (CNN) that produces three directional velocities from three flow encodings, without requiring a fourth reference scan measuring background phase.

Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: a phantom study.

European radiology
OBJECTIVES: To compare image quality between a deep learning image reconstruction (DLIR) algorithm and conventional iterative reconstruction (IR) algorithms in dual-energy CT (DECT) and to assess the impact of these algorithms on radiomics robustness...