Risk Management

Efficacy and neural mechanisms of a vibrotactile-enhanced, brain-controlled soft robotic glove for upper limb rehabilitation after stroke: a multicentre randomised controlled trial protocol.

INTRODUCTION: Soft robotic gloves (SRGs) integrated with brain-computer interfaces (BCIs) have demonstrated potential in facilitating motor recovery after stroke by enabling active, intention-driven rehabilitation. Emerging evidence suggests that incorporating vibrotactile stimulation (VTS) into SRG-BCI systems may further enhance sensorimotor feedback. The objective of this study is to evaluate t...

Integrating qHTS and QSAR Models to Identify Safe GPCR-Targeted Compounds: A Focus on hERG-Dependent Cardiotoxicity.

G-protein-coupled receptors (GPCRs) are a diverse family of seven-transmembrane domain receptors that play pivotal roles in various physiological and neurological processes by mediating extracellular signals through G proteins. Notable GPCRs such as ADRB2, CHRM1, DRD2, and HTR2A are important therapeutic targets linked to conditions ranging from asthma to schizophrenia. The human ether-à-go-go-rel...

STRATUM-OS: first step in the development and validation of the STRATUM tool based on multimodal data processing to assist surgery in patients affected by intra-axial brain tumours - observational study protocol.

INTRODUCTION: Integrated digital diagnostics can support complex surgeries in many anatomic sites, and brain tumour surgery represents one of the most...

Threat discrimination of real-world social interactions in schizotypal traits.

Threat detection is compromised across the schizophrenia spectrum, often revealed by paranoia and delusions. Threat difficulties extend to nonclinical...

Multi-encoder U-Net benchmarking for LiTS17 Liver-Tumor segmentation: accuracy-efficiency trade-offs across training durations.

OBJECTIVES: Accurate liver and tumor segmentation from CT is fundamental for diagnosis, treatment planning, and longitudinal monitoring of liver cance...

GutMIND: A multi-cohort machine learning framework for integrative characteristics of the microbiota-gut-brain axis in neuropsychiatric disorders.

Emerging evidence underscores bidirectional communication along the microbiota-gut-brain axis in neuropsychiatric disorders. However, the field lacks ...

Environmentally sustainable UV spectrophotometric-chemometric approach for simultaneous determination of budesonide, glycopyrrolate, and formoterol.

This study introduces a sustainable UV-spectrophotometric platform for the simultaneous and individual determination of budesonide (BUD), glycopyrrola...

AI-assisted video analysis of the Trendelenburg test: a feasibility study.

The Trendelenburg test is widely used to assess hip abductor function, but interpretation is typically subjective and only moderately reliable. Compen...

Use of smart glasses for ultrasound-guided radial arterial catheterisation in obese patients: study protocol for a single-centre randomised clinical trial in China.

INTRODUCTION: Ultrasound guidance improves the success rate and efficiency of radial arterial catheterisation (RAC). However, the procedure remains mo...

Scoping review of AI-driven wearable technologies for rehabilitation and functional assessment in Parkinson's disease: a protocol.

BACKGROUND: Parkinson's disease (PD) is a common neurodegenerative disorder characterised by high prevalence and disability rates, severely impairing ...

CLAIRE: a unified framework for reporting and assessing artificial intelligence in diagnostic imaging.

Artificial intelligence (AI) models for diagnostic imaging face reproducibility challenges due to inconsistent reporting. Existing guidelines also lac...

Clinical integration of artificial intelligence and mixed reality-based dynamic navigation for implant placement in the anterior maxilla.

OBJECTIVE: To report the first clinical application of an integrated workflow combining artificial intelligence (AI) with mixed reality-based dynamic ...

Feasibility of BMI-based sub-milliSievert low-dose CT in individualized detection of lung nodules.

OBJECTIVES: To evaluate the performance of a body mass index (BMI)-based sub-milliSievert low-dose CT (LDCT) protocol with multiple reconstruction alg...

Predicting neoadjuvant immunotherapy efficacy with machine learning models in non-small cell lung cancer: A systematic review and meta analysis.

BACKGROUND: The response of resectable non-small cell lung cancer (NSCLC) to neoadjuvant immunotherapy is heterogeneous. Machine learning can integrat...

Enhancing fairness and standardization in AI-versus-physician diagnostic comparisons: A scoping review.

OBJECTIVE: The growing number of studies directly comparing artificial intelligence (AI) to physicians in diagnostic tasks often focuses on performanc...

Protocol to perform cell-type-specific transcriptome-wide association study using scPrediXcan framework.

The scPrediXcan framework enables cell-type-specific transcriptome-wide association studies (TWASs) by integrating deep learning-based prediction of g...

Robotic-Arm Assisted Multi-Apical View 3-D Fusion of Echocardiography for Enhanced Left Ventricular Assessment Using Wavelet.

OBJECTIVE: Accurate assessment of left ventricular (LV) function using three-dimensional echocardiography (3-DE) remains limited by suboptimal image q...

Predicting the need for electroconvulsive therapy via machine learning trained on electronic health record data.

OBJECTIVES: Electroconvulsive therapy (ECT) is an effective treatment of severe manifestations of mental illness. Since delay in initiation of ECT can...

Artificial intelligence to improve the detection and risk stratification of acute pulmonary embolism (AID-PE): protocol for a pragmatic quasi-experimental comparator study.

INTRODUCTION: Pulmonary embolism (PE) is a potentially fatal condition requiring timely diagnosis and treatment. CT pulmonary angiography (CTPA) is th...

Applicability of mitotic figure counting by deep learning: a development and pan-cancer validation study.

Mitotic figure counting is an established measure of cell proliferation that is included in grading systems. We developed a deep learning method for m...