AIMC Topic: Humans

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A fine-tuning deep residual convolutional neural network for emotion recognition based on frequency-channel matrices representation of one-dimensional electroencephalography.

Computer methods in biomechanics and biomedical engineering
Emotion recognition (ER) plays a crucial role in enabling machines to perceive human emotional and psychological states, thus enhancing human-machine interaction. Recently, there has been a growing interest in ER based on electroencephalogram (EEG) s...

A Scoping Review of the Use of Robotics Technologies for Supporting Social-Emotional Learning in Children with Autism.

Journal of autism and developmental disorders
This scoping review synthesises the current research into robotics technologies for promoting social-emotional learning in children with autism spectrum disorder. It examines the types of robotics technologies employed, their applications, and the ga...

Opportunities and challenges for deep learning in cell dynamics research.

Trends in cell biology
The growth of artificial intelligence (AI) has led to an increase in the adoption of computer vision and deep learning (DL) techniques for the evaluation of microscopy images and movies. This adoption has not only addressed hurdles in quantitative an...

Machine Learning Web Application for Predicting Functional Outcomes in Patients With Traumatic Spinal Cord Injury Following Inpatient Rehabilitation.

Journal of neurotrauma
Accurately predicting functional outcomes in patients with spinal cord injury (SCI) helps clinicians set realistic functional recovery goals and improve the home environment after discharge. The present study aimed to develop and validate machine lea...

A framework for generalizable neural networks for robust estimation of eyelids and pupils.

Behavior research methods
Deep neural networks (DNNs) have enabled recent advances in the accuracy and robustness of video-oculography. However, to make robust predictions, most DNN models require extensive and diverse training data, which is costly to collect and label. In t...

A quality assurance framework for routine monitoring of deep learning cardiac substructure computed tomography segmentation models in radiotherapy.

Medical physics
BACKGROUND: For autosegmentation models, the data used to train the model (e.g., public datasets and/or vendor-collected data) and the data on which the model is deployed in the clinic are typically not the same, potentially impacting the performance...

Learning Curve in Robotic Stereoelectroencephalography: Single Platform Experience.

World neurosurgery
BACKGROUND: Learning curve, training, and cost impede widespread implementation of new technology. Neurosurgical robotic technology introduces challenges to visuospatial reasoning and requires the acquisition of new fine motor skills. Studies detaili...

Deep Learning-Assisted Segmentation and Classification of Brain Tumor Types on Magnetic Resonance and Surgical Microscope Images.

World neurosurgery
OBJECTIVE: The primary aim of this research was to harness the capabilities of deep learning to enhance neurosurgical procedures, focusing on accurate tumor boundary delineation and classification. Through advanced diagnostic tools, we aimed to offer...

Robot-assisted radical prostatectomy using the avatera systemâ„¢: a prospective pilot study.

Minerva urology and nephrology
BACKGROUND: Robot-assisted radical prostatectomy is a minimally invasive, safe procedure preferred in the management of localized prostate cancer. In this study, we present our initial experience with the avatera system (avateramedical GmbH, Jena, Ge...