Critical Care

KT-YOLO: A multi-convolution kernel collaboration model for dense Hu sheep behavior detection.

Computer vision has been extensively applied to sheep behavior detection in recent years. However, the dense distribution of Hu sheep poses detection challenges, while imbalanced behavioral categories in datasets affect classification accuracy for detection tasks in intensive farming scenarios, resulting in high misclassification rates. Current models often rely on over-parameterization to achieve...

Automated robotic arm system for real-time multi-parameter quality assessment of raw milk in cheese manufacturing.

Ensuring the quality of raw milk is critical for consistent cheese manufacturing, yet traditional laboratory-based testing methods are slow, labor-intensive, and impractical for decentralized rural supply chains. This study presents a portable, fully automated robotic arm system for real-time, multi-parameter milk quality assessment. The system integrates pH, total dissolved solids (TDS), temperat...

Early Risk Stratification for Mechanical Ventilation in Acute Ischemic Stroke: Development and Validation of a Simplified Clinical Score.

Background and PurposeMechanical ventilation (MV) occurs in a substantial subset of acute ischemic stroke (AIS) hospitalizations and is associated wit...

Interpretable machine learning methods based on oscillometry and electric modeling for the diagnostic of respiratory dysfunction in silicosis.

PURPOSE: Silicosis, the most dangerous and common lung illness associated with breathing in mineral dust, is a significant health concern. Although Re...

Diffusion-Based Generative Model With Scaffold-Hopping Strategy Yields Highly Potent Bioactive Molecules.

As a critical step in drug discovery, lead optimization is a profoundly complex endeavor with a notoriously high failure rate, as it necessitates the ...

Smartphone-Based Pupillometry for Noninvasive Detection of Elevated Intracranial Pressure in Nepali Acute Brain Injury Patients: A Pilot Study.

BACKGROUND: Intracranial hypertension is a life-threatening complication of acute brain injuries such as traumatic brain injury (TBI), subarachnoid he...

TIME: A Taylor-Inspired Mixed-Effects Model for IDH Prediction.

BACKGROUND: Intradialytic hypotension (IDH) is a critical complication in hemodialysis that increases morbidity and treatment risks, yet existing mach...

GRACE-MORE: A Motion-Resolved Golden-Angle Radial CEST MRI Technique for Free-Breathing Abdominal Imaging.

PURPOSE: To develop a motion-resolved acquisition and reconstruction framework for motion-robust and spectrally reliable abdominal CEST imaging under ...

On the usage of artificial intelligence for identifying main attributes and predicting neonatal sepsis.

During the neonatal period, newborns are more susceptible to developing conditions and diseases due to their fragility of the transition and adaptatio...

The AI adoption paradox in Chinese medical education: a multi-institution cross-sectional study of usage patterns, critical literacy gaps, and adoption profiles among 3,194 undergraduate medical students.

BACKGROUND: Artificial intelligence tools are widely used by Chinese medical students, yet systematic evidence on usage patterns, critical literacy ga...

Predicting mortality risk in pediatric severe pneumonia using a CNN-BiLSTM model with dynamic clinical indicators.

BACKGROUND: Severe pneumonia is a leading cause of mortality in children under 5 years old, and there is currently a lack of reliable biomarkers for e...

An interpretable multi-task learning model for effluent quality and greenhouse gas emissions prediction in wastewater treatment plants.

Wastewater treatment plants (WWTPs) face growing pressure to comply with regulatory effluent standards while reducing greenhouse gas (GHG) emissions a...

Developing and validating an electronic health record-embedded AI model for managing multimorbid hospitalisation risk in patients with chronic RESpiratory disease (AiRES): a study protocol.

BACKGROUND: Chronic respiratory diseases (CRDs), such as asthma and chronic obstructive pulmonary disease (COPD), are heterogeneous conditions with a ...

Deep-learning time-series anomaly detection of acute kidney injury from creatinine-eGFR trajectories in the ICU.

Acute kidney injury (AKI) is common in the intensive care unit (ICU), and fixed creatinine thresholds may miss clinically relevant dynamics. We tested...

Hybrid attention-based multi-class classification of Ethiopian legal texts using deep learning.

The exponential growth of legal documents in Ethiopia has created an urgent need for efficient and accurate automated classification systems tailored ...

Deep residual network fusing CT images and clinical variables to predict lung adenocarcinoma aggressiveness.

BACKGROUND: Lung adenocarcinoma presenting as ground-glass nodules (GGNs) comprises three invasive subtypes (adenocarcinoma in situ [AIS], minimally i...

Interpretable machine learning for predicting in-hospital mortality in COPD ICU patients: a rigorous validation across time and geography.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a common reason for admission to the intensive care unit (ICU), where accurate risk strati...

Deep learning-based 3D leukocyte differentiation using label-free higher harmonic generation microscopy.

BACKGROUND: Both in clinical practice and translational research, cell differentiation of leukocytes provides important diagnostic information and ins...

ARDSMLpred: A machine learning model for predicting SCAP-associated ARDS based on MIMIC-IV database.

BACKGROUND: Early identification of acute respiratory distress syndrome (ARDS) in severe community-acquired pneumonia (SCAP) are crucial for reducing ...

Multi-omics precision diagnosis of brucellosis: Advances in biomarker discovery and clinical application.

Brucellosis, a neglected zoonosis caused by intracellular Brucella bacteria, remains a formidable global public health challenge, especially in develo...