Deep Learning Model Predicts Lung Cancer Risk from Single LDCT Scan
Deep learning model Sybil predicts lung cancer risk from a single LDCT scan, aiding personalized screening strategies. 🩻💡
Towards determining clinical factors influencing critical structure identification using Artificial Intelligence.
AI enhances laparoscopic surgery insights, analyzing 481 videos to reveal surgeon experience impacts on anatomy visualization. 📊🔍
Tombot Raises $6.1 Million for Robotic Companion Puppy
Tombot secures $6.1 million for a robotic puppy, aiding mental health for seniors with dementia. 🐶💰
CT derived fractional flow reserve: Part 2 – Critical appraisal of the literature.
CT-FFR enhances coronary evaluations, showing high diagnostic accuracy and clinical relevance. Key insights from recent literature reviewed. 📊💡
DeepSeek-R1: A New Era in Healthcare AI
DeepSeek-R1 shows potential in enhancing healthcare delivery and patient engagement. Challenges remain for its clinical integration. 🏥🤖
Comprehensive statistical and machine learning framework for identification of metabolomic biomarkers in breast cancer.
Innovative metabolomic biomarkers for early breast cancer detection show 83% accuracy using machine learning techniques. 📊🔬
Doccla Partners with Gophr for Efficient Medical Device Delivery
Doccla partners with Gophr for efficient medical device delivery. 🚚📦 Real-time tracking enhances patient care and logistics.
The Machine Learning Models in Major Cardiovascular Adverse Events Prediction Based on Coronary Computed Tomography Angiography: Systematic Review.
Machine learning enhances CCTA in predicting major cardiovascular events, achieving AUROC up to 0.8444. 📊❤️
Novant Health Nurse Highlights Impact of Technology on Nursing Careers
Novant Health nurse Panissa Caldwell discusses how technology enhances nursing education and practice. 📚💻
Development of a machine learning-based predictive nomogram for screening children with juvenile idiopathic arthritis: a pseudo-longitudinal study of 223,195 children in the United States.
Machine learning predicts juvenile idiopathic arthritis in children: 223,195 analyzed, AUC up to 0.9130! 📊🤖