AIMC Topic: Humans

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Technical Development of the CeCi Social Robot.

Sensors (Basel, Switzerland)
This research presents the technical considerations for implementing the CeCi (Computer Electronic Communication Interface) social robot. In this case, this robot responds to the need to achieve technological development in an emerging country with t...

Energy Expenditure Estimation in Children, Adolescents and Adults by Using a Respiratory Magnetometer Plethysmography System and a Deep Learning Model.

Nutrients
PURPOSE: Energy expenditure is a key parameter in quantifying physical activity. Traditional methods are limited because they are expensive and cumbersome. Additional portable and cheaper devices are developed to estimate energy expenditure to overco...

Evaluation Model of New Socialist Countryside Construction Based on a Multilayer Neural Network in a Complex Environment.

Journal of environmental and public health
The creation of a deep neural network model with numerous hidden layers enables the layer-by-layer extraction of features from the input high-dimensional data, enabling the identification of the data's low-dimensional nested structure and the develop...

Deep Learning Coronary Artery Calcium Scores from SPECT/CT Attenuation Maps Improve Prediction of Major Adverse Cardiac Events.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Low-dose ungated CT attenuation correction (CTAC) scans are commonly obtained with SPECT/CT myocardial perfusion imaging. Despite the characteristically low image quality of CTAC, deep learning (DL) can potentially quantify coronary artery calcium (C...

Artificial intelligence in hematological diagnostics: Game changer or gadget?

Blood reviews
The future of clinical diagnosis and treatment of hematologic diseases will inevitably involve the integration of artificial intelligence (AI)-based systems into routine practice to support the hematologists' decision making. Several studies have sho...

Predicting the evolution trajectory of population-driven connectional brain templates using recurrent multigraph neural networks.

Medical image analysis
The mapping of the time-dependent evolution of the human brain connectivity using longitudinal and multimodal neuroimaging datasets provides insights into the development of neurological disorders and the way they alter the brain morphology, structur...

Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Supervised deep learning has become a standard approach to solving medical image segmentation tasks. However, serious difficulties in attaining pixel-level annotations for sufficiently large volumetric datasets in real-life applications have highligh...

Degrees of algorithmic equivalence between the brain and its DNN models.

Trends in cognitive sciences
Deep neural networks (DNNs) have become powerful and increasingly ubiquitous tools to model human cognition, and often produce similar behaviors. For example, with their hierarchical, brain-inspired organization of computations, DNNs apparently categ...