Global trends and hotspots in AI applications for CT detection of chronic obstructive pulmonary disease: A bibliometric analysis from 2012 to 2024.
AI in CT Detection of COPD: 189 Publications, 37.83% Growth 📈🌍
Intelligent Sensing: The Emerging Integration of Machine Learning and Soft Sensors Based on Hydrogels and Ionogels.
Exploring intelligent sensing: Machine learning meets hydrogels & ionogels for health monitoring and beyond! 🤖💧
Evaluating the Performance of ChatGPT-4V in Detecting Inflammatory Magnetic Resonance Imaging Findings of Sacroiliitis: Potentials, Challenges, and Limitations.
AI in MRI: ChatGPT-4V shows 95.5% sensitivity for bone marrow edema in sacroiliitis detection. Limitations noted. 📊🩻
Anaerobic microbial degradation of persistent organic pollutants in aquatic sediments: implications of climate change.
Anaerobic microbes can boost POP degradation by 50% under moderate warming, aiding climate change mitigation. 🌍🔬
Artificial Intelligence in Supportive Oncology and Symptom Management Opportunities.
AI in Oncology: Enhancing Symptom Management & Patient Care 🤖💊
The ADVANCE toolkit: Automated descriptive video annotation in naturalistic child environments.
Revolutionizing video analysis in child behavior studies with ADVANCE toolkit: 2-5 individuals tracked accurately! 🎥👶
SHAP-based interpretable machine learning for injury risk prediction in university football players: a multi-dimensional data analysis approach.
SHAP analysis reveals key injury risk factors in university football players: stress, sleep, and balance. ⚽📊
Preparing for the Next Influenza Pandemic: Vaccine Progress, Challenges, and Prospects.
Influenza pandemic preparedness hinges on effective vaccines amid rapid mutations and emerging strains. Key advancements are crucial. 🦠💉
Deep learning with refined single candidate optimizer for early polyp detection.
Revolutionary deep learning model enhances early polyp detection in colonoscopy, improving accuracy and precision significantly! 📊🔍
Machine learning models incorporating genotype and ancestry improve severe asthma risk prediction.
Machine learning enhances severe asthma risk prediction by integrating genotype and ancestry data. AUC improved from 0.693 to 0.729! 📊✨