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The Effect of Workload and Task Priority on Multitasking Performance and Reliance on Level 1 Explainable AI (XAI) Use.

Human factors
ObjectiveThis study investigates the effects of workload and task priority on multitasking performance and reliance on Level 1 Explainable Artificial Intelligence (XAI) systems in high-stakes decision environments.BackgroundOperators in critical sett...

Association between muscle mass assessed by an artificial intelligence-based ultrasound imaging system and quality of life in patients with cancer-related malnutrition.

Nutrition (Burbank, Los Angeles County, Calif.)
INTRODUCTION: Emerging evidence suggests that diminished skeletal muscle mass is associated with lower health-related quality of life (HRQOL) in individuals with cancer. There are no studies that we know of in the literature that use ultrasound syste...

Automated Fast Prediction of Bone Mineral Density From Low-dose Computed Tomography.

Academic radiology
BACKGROUND: Low-dose chest CT (LDCT) is commonly employed for the early screening of lung cancer. However, it has rarely been utilized in the assessment of volumetric bone mineral density (vBMD) and the diagnosis of osteoporosis (OP).

Super-resolution deep learning reconstruction for improved quality of myocardial CT late enhancement.

Japanese journal of radiology
PURPOSE: Myocardial computed tomography (CT) late enhancement (LE) allows assessment of myocardial scarring. Super-resolution deep learning image reconstruction (SR-DLR) trained on data acquired from ultra-high-resolution CT may improve image quality...

AI-based personalized real-time risk prediction for behavioral management in psychiatric wards using multimodal data.

International journal of medical informatics
BACKGROUND: Suicide is a major global health issue, with approximately 700,000 deaths annually (WHO). In psychiatric wards, managing harmful behaviors such as suicide, self-harm, and aggression is essential to ensure patient and staff safety. However...

Dual-pathway EEG model with channel attention for virtual reality motion sickness detection.

Journal of neuroscience methods
BACKGROUND: Motion sickness has been a key factor affecting user experience in Virtual Reality (VR) and limiting the development of the VR industry. Accurate detection of Virtual Reality Motion Sickness (VRMS) is a prerequisite for solving the proble...

SSAT-Swin: Deep Learning-Based Spinal Ultrasound Feature Segmentation for Scoliosis Using Self-Supervised Swin Transformer.

Ultrasound in medicine & biology
OBJECTIVE: Scoliosis, a 3-D spinal deformity, requires early detection and intervention. Ultrasound curve angle (UCA) measurement using ultrasound images has emerged as a promising diagnostic tool. However, calculating the UCA directly from ultrasoun...

Diagnostic Accuracy of a Deep Learning Algorithm for Detecting Unruptured Intracranial Aneurysms in Magnetic Resonance Angiography: A Multicenter Pivotal Trial.

World neurosurgery
BACKGROUND: Intracranial aneurysm rupture is associated with high mortality and disability rates. Early detection is crucial, but increasing diagnostic workloads place significant strain on radiologists. We evaluated the efficacy of a deep learning a...

DCAlexNet: Deep coupled AlexNet for micro facial expression recognition based on double face images.

Computers in biology and medicine
Facial Micro-Expression Recognition (FER) presents challenges due to individual variations in emotional intensity and the complexity of feature extraction. While apex frames offer valuable emotional information, their precise role in FER remains uncl...

Artificial intelligence (ChatGPT 4.0) vs. Human expertise for epileptic seizure and epilepsy diagnosis and classification in Adults: An exploratory study.

Epilepsy & behavior : E&B
AIMS: Artificial intelligence (AI) tools like ChatGPT hold promise for enhancing diagnostic accuracy and efficiency in clinical practice. This exploratory study evaluates ChatGPT's performance in diagnosing and classifying epileptic seizures, epileps...