Critical Care

Multiclass Classification of Renal Tumor Subtypes: Addressing Diagnostic Challenges Using a Texture-Informed Deep Hybrid CNN-Transformer.

Accurate histopathological classification of renal cell carcinoma (RCC), along with its distinction from benign mimickers, is essential for precision oncology and optimal patient management. The morphological complexity and subtle differences among RCC tumors pose significant diagnostic challenges, often resulting in notable inter-observer variability. This paper presents a novel texture-informed ...

Automatic monitoring of single-wall MAPSE by transesophageal echocardiography for tracking global left ventricular function irrespective of regional hypokinesia: a secondary analysis.

BACKGROUND: Measuring mitral annular plane systolic excursion (MAPSE) serially in a single wall may be an effective method for monitoring global left ventricular (LV) function, especially when automated with a novel deep learning method using transesophageal echocardiography, called autoMAPSE. However, this assumption is untested in postoperative ICU patients, and the impact of regional wall motio...

Interpretable machine learning for early prediction of sepsis-induced coagulopathy: a multicenter retrospective development and validation study.

Nephrology: What You May Have Missed in 2025.

This article highlights some important nephrology studies published in 2025 that are relevant for many nonnephrologist physicians. Two studies examine...

Novel machine learning-based approach to identify viral biomarkers of human respiratory emissions from oral and nasal metagenomes.

Humans spend approximately 90% of their lives in built environments, making virus transmission indoors a key determinant of health. Environmental samp...

In-hospital outcomes and cost-effectiveness of transcatheter aortic valve replacement among younger patients: a double/debiased machine learning approach using electronic health records in Germany.

BACKGROUND: The prevalence of severe symptomatic aortic stenosis is increasing with population aging. Although surgical aortic valve replacement (SAVR...

Evaluating Artificial Intelligence for Sepsis Prediction in Emergency Departments: A Systematic Review and Meta Analysis.

Risk prediction in spine surgery: a scoping review of traditional models, artificial intelligence, and the challenge of clinical translation.

BACKGROUND: Accurate perioperative risk stratification is essential to patient safety and informed consent in spine surgery. Traditional regression-ba...

Synergistic Multi-Magnification Fusion Network for Tongue Image-Based Oral Cancer Diagnosis.

Oral cancer represents a critical global public health concern, where accurate and timely early detection is paramount. While deep learning on non-inv...

Performance of Machine Learning Models for Sepsis and Stroke Detection Using EMS Data.

OBJECTIVES: Early sepsis and stroke recognition by emergency medical services (EMS) improves triage, treatment, and patient outcomes. Machine learning...

Development of a machine learning-based survival prediction model for ALS inclusive of the advanced-stage population.

OBJECTIVE: Develop a machine learning-based model for survival prediction in ALS, including advanced-stage patients (≤50% predicted normal vital capac...

Explainable machine learning for predicting mechanical ventilation in Alzheimer's patients with pneumonia: a SHAP-guided XGBoost nomogram based on MIMIC-IV.

Alzheimer's disease (AD) patients are particularly vulnerable to pneumonia and subsequent respiratory failure due to neurodegeneration-induced dysphag...

[Expert consensus on optimizing the multidisciplinary clinical pathways and management models for pulmonary function testing].

The promotion and application of pulmonary function tests (PFTs) in China have achieved preliminary success;however, numerous deficiencies persist in ...

A machine learning approach to predicting dyspnea with noninvasive biomarkers.

Dyspnea is the subjective sensation of breathing discomfort. This symptom is highly prevalent in patients with chronic and critical illness, and its p...

The multi-target cardiac protection mechanism of butylphthalide and AI-driven precision medicine: From molecular basis to clinical translation.

Butylphthalide (NBP) is a lipophilic small-molecule drug characterized by its multi-target and multi-pathway regulatory properties. Initially employed...

Association between aspirin use and mortality in critically ill patients with heart failure: a retrospective study using the MIMIC-IV database.

BACKGROUND: Despite the established antithrombotic benefits of aspirin in cardiovascular disease, its efficacy remains controversial for critically il...

AI preference prediction beyond substituted judgement: enhancing best interest decision-making.

Tracking patient preferences is vital to medical decision-making, but evidence suggests that the standard method for tracking the preferences of incap...

Association between the monocyte-to-lymphocyte ratio and 28-day all-cause mortality in sepsis-associated delirium patients: a retrospective study and machine learning.

Development of ICU nurses' compassion fatigue classification model: an explainable machine learning algorithm.

Computed tomography in ARDS, from morphological insights to AI-powered multi-modal analysis: a narrative review.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a critical clinical condition characterized by acute respiratory failure and high mortality....