Identifying and Reporting Child abuse

Patient-specific instantaneous spatial temperature maps for MR-guided laser interstitial thermal therapy using a physics-assisted deep learning framework.

PURPOSE: Accurate prediction of the laser energy absorption and corresponding thermal spread is essential for safe and effective outcomes in magnetic resonance-guided laser interstitial thermal therapy (MRgLITT), as it enables clinicians to anticipate thermal spread and ensure complete ablation of the epileptogenic focus while minimizing collateral damage. However, current planning tools rely on s...

A responsible AI framework for infection surveillance in low-resource settings: ethics, opportunities and threats for LMICs (EOT-LMICs).

The integration of artificial intelligence (AI) into infection surveillance represents a significant shift in public health; however, most AI frameworks and implementation guidance neglect the distinct constraints of low- and middle-income countries (LMICs). This commentary presents the ethics, opportunities, and threats for LMICs (EOT-LMICs) framework, developed through synthesis of the literatur...

A robust multi-view support vector machine with the RoBoSS loss function.

Multi-view learning capitalizes the multiple representations of data and integrates their heterogeneous information to improve the learning performanc...

Medium-aware multi-task learning for robust bacterial identification using Raman spectroscopy.

Raman spectroscopy has demonstrated significant potential for rapid bacterial identification; however, bacterial spectral signatures are frequently co...

A scalable EEG-based spatial neglect detection system in augmented reality for stroke patients.

BACKGROUND: Spatial neglect is a common visuospatial attention disorder following a stroke. To overcome weaknesses associated with classic pen-and-pap...

Jo-SNC: Combating Noisy Labels Through Fostering Self- and Neighbor-Consistency.

Label noise is pervasive in various real-world scenarios, posing challenges in supervised deep learning. Deep networks are vulnerable to such label-co...

Multi-output LSTM-based prediction of postoperative delirium: integrating baseline and perioperative data for enhanced risk stratification in older spine surgery patients.

INTRODUCTION: Postoperative delirium (POD) adversely affects clinical outcomes among older adults undergoing spine surgery. However, existing predicti...

Data-driven prediction of ionic conductivity in solid-state electrolytes with machine learning and large language models.

Solid-state electrolytes (SSEs) are attractive for next-generation lithium-ion batteries due to improved safety and stability, but their low room-temp...

WMambaFuse: an infrared and visible image fusion network based on wavelet mamba.

Infrared and visible image fusion aims to generate a fusion image that integrates complementary information from both modalities. Current deep learnin...

Leveraging artificial intelligence to accelerate competency development in diagnostic reasoning of medical students.

INTRODUCTION: Diagnostic errors remain a critical patient safety concern, yet teaching diagnostic reasoning is challenging due to its complex, context...

External-knowledge enhanced dual encoder and contrastive learning for aspect sentiment triplet extraction.

Aspect Sentiment Triplet Extraction (ASTE) is an emerging subtask of Aspect-Based Sentiment Analysis (ABSA), aiming to extract aspect terms, opinion t...

SCADA: Sparse cross attention for domain adaptive semantic segmentation.

Unsupervised domain adaptive (UDA) semantic segmentation intends to perform satisfactory dense prediction on unannotated target (real-world) images by...

AI-powered biomechanical modeling for ACL-reconstructed knees: predicting knee joint contact forces via computer vision and deep learning.

BACKGROUND: Patients undergoing anterior cruciate ligament reconstruction (ACLR) are at high risk of osteoarthritis or secondary injuries, with abnorm...

Contribution of social determinants to symptoms of generalized anxiety disorder.

Symptoms of anxiety are known to be triggered by a range of life context factors including early life trauma, poor sleep quality, infrequent exercise,...

scDCL: A multi-view single-cell RNA sequencing clustering method based on dual contrastive learning.

Single-cell RNA sequencing (scRNA-seq) has become a pivotal tool for resolving cellular heterogeneity and uncovering novel cell states. While deep lea...

Generative AI in EFL contexts: Q-methodological analysis of cognitive dissonance patterns and self-regulation strategies among Chinese tertiary learners.

Generative Artificial Intelligence (GenAI) has recently attracted growing attention in English as a Foreign Language (EFL) education, offering opportu...

Het2Gene: a phenotype-driven model for gene prioritization by heterogeneous graph embedding.

Mendelian genetic diseases pose a significant global health burden. Early identification of causative genes is crucial for halting disease progression...

PGST: A prototype-guided parameter-efficient network for spatial transcriptomics prediction.

Spatial transcriptomics (ST) aims to decode spatially resolved gene expression patterns while preserving tissue morphology. Current methods tend to us...

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...

A physics-informed dual-branch fusion network for quantitative determination of total phosphorus in water using near-infrared spectroscopy.

BACKGROUND: Quantitative determination of total phosphorus (TP), an indirectly absorbing aquatic indicator, using near-infrared (NIR) spectroscopy is ...