AIMC Topic: Young Adult

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Classifying Routine Clinical Electroencephalograms With Multivariate Iterative Filtering and Convolutional Neural Networks.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Electroencephalogram (EEG) is widely used in basic and clinical neuroscience to explore neural states in various populations, and classifying these EEG recordings is a fundamental challenge. While machine learning shows promising results in classifyi...

Using drawings and deep neural networks to characterize the building blocks of human visual similarity.

Memory & cognition
Early in life and without special training, human beings discern resemblance between abstract visual stimuli, such as drawings, and the real-world objects they represent. We used this capacity for visual abstraction as a tool for evaluating deep neur...

CT-based radiomics analysis of different machine learning models for differentiating gnathic fibrous dysplasia and ossifying fibroma.

Oral diseases
OBJECTIVE: In this study, our aim was to develop and validate the effectiveness of diverse radiomic models for distinguishing between gnathic fibrous dysplasia (FD) and ossifying fibroma (OF) before surgery.

CT-Net: an interpretable CNN-Transformer fusion network for fNIRS classification.

Medical & biological engineering & computing
Functional near-infrared spectroscopy (fNIRS), an optical neuroimaging technique, has been widely used in the field of brain activity recognition and brain-computer interface. Existing works have proposed deep learning-based algorithms for the fNIRS ...

Practical measurements distinguishing physiological and pathological stereoelectroencephalography channels based on high-frequency oscillations in the human brain.

Epilepsia open
OBJECTIVE: The present study aimed to identify various distinguishing features for use in the accurate classification of stereoelectroencephalography (SEEG) channels based on high-frequency oscillations (HFOs) inside and outside the epileptogenic zon...

Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical study.

EBioMedicine
BACKGROUND: Dengue epidemics impose considerable strain on healthcare resources. Real-time continuous and non-invasive monitoring of patients admitted to the hospital could lead to improved care and outcomes. We evaluated the performance of a commerc...

Evaluating initial usability of a hand augmentation device across a large and diverse sample.

Science robotics
The advancement of motor augmentation and the broader domain of human-machine interaction rely on a seamless integration with users' physical and cognitive capabilities. These considerations may markedly fluctuate among individuals on the basis of th...

Panoramic Radiography in the Evaluation of the Relationship of Maxillary Molar Teeth and Maxillary Sinuses on the Deep Learning Models Improved with the Findings Obtained by Cone Beam Computed Tomography.

Nigerian journal of clinical practice
BACKGROUND: Panoramic radiography (PR) is available to determine the contact relationship between maxillary molar teeth (MMT) and the maxillary sinus floor (MSF). However, as PRs do not provide clear and detailed anatomical information, advanced imag...

Evaluation of Biomechanical and Mental Workload During Human-Robot Collaborative Pollination Task.

Human factors
OBJECTIVE: The purpose of this study is to identify the potential biomechanical and cognitive workload effects induced by human robot collaborative pollination task, how additional cues and reliability of the robot influence these effects and whether...