Pain Management

Mamba ECIS for power marketing customer behavior forecasting using multimodal deep learning.

As the energy industry faces increasingly complex changes in demand and external environmental impacts, traditional forecasting models struggle to effectively integrate multi-source and multimodal data while maintaining interpretability. Therefore, a forecasting model capable of processing multimodal data simultaneously and capturing long-term dependencies is needed. Mamba-ECIS enhances the abilit...

Perilesional neuromodulation replaces lost sensorimotor function in persons with spinal cord injury.

Spinal cord injury (SCI) results in permanent impairment of sensory, motor and autonomic function. Epidural electrical stimulation (EES) applied below the lesion can restore voluntary movement, autonomic function and locomotion following chronic SCI. However, impaired sensation below the SCI does not improve during the application of sublesional EES. Here we present first-in-human results demonstr...

Lower Back Muscle Fatigue Recognition Based on the Fusion-Information of Multi-Channel sEMG and NIRS Simultaneous Recordings.

Accurate recognition of muscle fatigue in the lower back is essential for preventing low back pain and reducing the risk of occupational injuries. How...

AI-Based Personalized Therapy With Clinical Intelligence and Radiomics (SPOILS) for Patients With Low Back Pain: Prospective Observational Study.

BACKGROUND: Low back pain (LBP) is a leading cause of disability worldwide, affecting people of all ages while showing increasing prevalence among you...

A SEM-ANN analysis to examine impact of AI overreliance through a social cognitive lens: the roles of dependency, FOMO, addiction, and anxiety.

The rapid integration of artificial intelligence (AI) has transformed how people learn, work, and make decisions, while raising growing concerns about...

Bridging Population Patterns and Individual Prediction: Framework for Prospective Multimorbidity Study.

BACKGROUND: Multimorbidity has become a major global public health challenge. However, existing research primarily emphasizes the identification of di...

Clinical and pathologic correlations of machine learning quantification of Aβ deposits across 3 brain regions of decedents with Alzheimer disease.

Machine learning enables scalable quantification of neuropathology, offering deeper phenotyping of Alzheimer's disease (AD). In this validation study,...

Quantification of Ki-67 labeling index in pediatric brain tumor immunohistochemistry images.

Quantification of the Kiel 67 (Ki-67) labeling index (LI) is critical for assessing proliferation and prognosis in tumors but manual scoring remains a...

Incidence rate of and risk factors for anorectal diseases in primiparae in the first 6 weeks following delivery.

OBJECTIVES: We aimed to explore the incidence rate of and risk factors for anorectal diseases in primiparae in the first 6 weeks following delivery, a...

Applications of artificial intelligence algorithms in ultrasound-based kidney stone detection, classification, prediction, and management: a systematic review.

BACKGROUND: Kidney stones are a prevalent urological condition with significant global burden, often diagnosed using ultrasound (US) as a first-line m...

Investigating the effects of back-support exoskeletons on work posture: An automated AI-based NIOSH measurement using YOLO.

Work-related musculoskeletal disorders (WMSDs) are prevalent among masons with 58% reporting lower back pain from repetitive lifting and sustained tru...

Survival prediction for bladder cancer using multimodal data with quantum neural networks and transformer architectures.

To address the challenges of cross-modal information fusion in high-dimensional multimodal medical data for cancer prognosis, this study presents a hy...

Modeling inter-slice dependencies with temporal graph learning for Alzheimer's disease.

Accurate and early diagnosis of Alzheimer's disease (AD) remains a major clinical challenge, particularly in distinguishing mild cognitive impairment ...

Assessing the clinical competence of large language models for tobacco use disorder: A multi-domain expert evaluation.

BACKGROUND: Tobacco use disorder (TUD) remains the leading preventable cause of death globally, yet fewer than one-third of users receive guideline-co...

Smartphone addiction and temporomandibular disorders among university students: A machine learning based multiple regression analysis study.

Temporomandibular disorders (TMD) involve complex interactions among behavioral and musculoskeletal factors, and machine learning (ML) has increasingl...

Mixture-of-Skip-Connection Deep Learning Model to Classify Stroke Severity from Diffusion Weighted Imaging Based on NIHSS.

The National Institutes of Health Stroke Scale (NIHSS) is a quantitative tool, grading neurological deficits and guiding acute stroke management; howe...

Unveiling state-specific neural dynamics in migraine with and without depressive symptom: a hidden Markov model and interpretable machine learning approach.

Random forest models accurately classify synthetic opioids using high-dimensionality mass spectrometry datasets.

Detection of novel threat agents presents several challenges, a principle one being the development of untargeted methods to screen an increasing numb...

Automated Annotation of Pain Chronicity in Patients With Back Pain by Using Electronic Health Records: Retrospective Study.

BACKGROUND: Chronic back pain is a severe health condition with underlying biopsychosocial factors that make diagnosis difficult, and pain chronicity ...

Neural Network-Based Detection of Adulterants in Opioid Samples Using IR Absorption Spectroscopy.

Community-based drug checking services are challenged in their ability to reliably detect low concentration adulterants that are increasingly present ...