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Chronic neck pain is a leading cause of disability worldwide, and current treatment selection remains largely trial and error. We present a machine learning framework that uses electroencephalography to predict treatment efficacy in…

This study is concerned with the application of virtual reality (VR) in rehabilitation programs for faulty neck posture which is a primary source of neck pain (NP). The latter is a highly prevalent musculoskeletal disorder that is…

This paper presents a new approach for training artificial neural networks using techniques for solving the constraint satisfaction problem (CSP). The quotient gradient system (QGS) is a trajectory-based method for solving the CSP. This…

Automatic pain intensity estimation possesses a significant position in healthcare and medical field. Traditional static methods prefer to extract features from frames separately in a video, which would result in unstable changes and peaks…

Spinal cord stimulation (SCS) is a therapeutic approach used for the management of chronic pain. It involves the delivery of electrical impulses to the spinal cord via an implanted device, which when given suitable stimulus parameters can…

Chronic Low Back Pain (CLBP) afflicts millions globally, significantly impacting individuals' well-being and imposing economic burdens on healthcare systems. While artificial intelligence (AI) and deep learning offer promising avenues for…

Convolutional Neural Networks (CNNs) have advanced existing medical systems for automatic disease diagnosis. However, there are still concerns about the reliability of deep medical diagnosis systems against the potential threats of…

Purpose: Radiotherapy commonly relies on CT, but there is growing interest in using hybrid PET/MR. Therefore, dedicated hardware setups have been proposed for PET/MR systems which enable imaging in radiotherapy treatment position. These…

Background and Purpose: Voxel-based analysis (VBA) helps to identify dose-sensitive regions by aligning individual dose distributions within a common coordinate system (CCS). Accurate deformable image registration (DIR) is essential for…

Head pose estimation is a crucial problem for many tasks, such as driver attention, fatigue detection, and human behaviour analysis. It is well known that neural networks are better at handling classification problems than regression…

Machine learning offers a largely unexplored avenue for improving noisy disordered devices in physics using automated algorithms. Through simulations that include disorder in physical devices, particularly quantum devices, there is…

Work-Related Musculoskeletal Disorders continue to be a major challenge in industrial environments, leading to reduced workforce participation, increased healthcare costs, and long-term disability. This study introduces a human-sensitive…

Stress can increase the possibility of cognitive impairment and decrease the quality of life in older adults. Smart healthcare can deploy quantum machine learning to enable preventive and diagnostic support. This work introduces a unique…

Limited labeled data are available for the research of estimating facial expression intensities. For instance, the ability to train deep networks for automated pain assessment is limited by small datasets with labels of patient-reported…

To shorten the door-to-puncture time for better treating patients with acute ischemic stroke, it is highly desired to obtain quantitative cerebral perfusion images using C-arm cone-beam computed tomography (CBCT) equipped in the…

Seizure prediction has attracted a growing attention as one of the most challenging predictive data analysis efforts in order to improve the life of patients living with drug-resistant epilepsy and tonic seizures. Many outstanding works…

This paper presents the first investigation into the use of fully automated deep learning framework for assessing neonatal postoperative pain. It specifically investigates the use of Bilinear Convolutional Neural Network (B-CNN) to extract…

Cone-beam computed tomography (CBCT) typically requires hundreds of X-ray projections, which raises concerns about radiation exposure. While sparse-view reconstruction reduces the exposure by using fewer projections, it struggles to achieve…

Plant diseases serve as one of main threats to food security and crop production. It is thus valuable to exploit recent advances of artificial intelligence to assist plant disease diagnosis. One popular approach is to transform this problem…

We propose a computationally efficient algorithm for seizure detection. Instead of using a purely data-driven approach, we develop a hybrid model-based/data-driven method, combining convolutional neural networks with factor graph inference.…