New Online Tool for Choosing AI Models for 3D Organ Imaging
New online tool aids in selecting AI models for 3D organ imaging, enhancing treatment accuracy for patients. 🩺💻
Establishing Research Priorities in a Tertiary Care Hospital: A Data-driven and Stakeholder-informed Approach.
Data-driven research priorities identified: 25% hospitalizations due to pregnancy-related conditions. Key focus on AI, telemedicine, and patient care. 📊🏥
MRI quantitative imaging biomarkers in differentiating brain parenchymal tuberculoma and lung cancer brain metastases.
MRI radiomics model distinguishes brain tuberculoma from lung cancer metastases with AUC up to 0.986. 📊🧠
Computer-vision based recognition of cervical spine stabilization during trauma resuscitation.
Innovative computer vision system detects cervical spine stabilization techniques with 91% accuracy during trauma resuscitation. 📊🩺
Tributes Paid to Professor Nick Hardiker, a Leader in Nursing Informatics
Professor Nick Hardiker, a respected leader in nursing informatics, has passed away. His contributions to the field will be greatly missed. 🕊️📚
Numerical computation of the stochastic hepatitis B model using feed forward neural network and real data.
Innovative hepatitis B model combines neural networks and real data, revealing key dynamics of virus transmission. 📊🦠
Collaboration Between NHS Confederation and Limbic to Enhance AI in Mental Health
NHS Confederation collaborates with Limbic to enhance AI in mental health services, aiming for improved access and care. 🤝🧠
Adaptive Visual Selection: Predictive Control of Visual Attention.
Exploring predictive control in visual attention: gaze behavior reflects cognitive states like expectation and learning. 🧠👀
Utilization of artificial intelligence in prostate cancer detection: a comprehensive review of innovations in screening and diagnosis.
AI revolutionizes prostate cancer detection, enhancing mpMRI accuracy and personalizing treatment strategies. Significant advancements detailed in our review. 📊🤖
Deep learning-enabled accurate assessment of gait impairments in Parkinson’s disease using smartphone videos.
Deep learning assesses Parkinson's gait via smartphone videos: AUC 0.87, F1 score 0.806, precision 73.68%. 📱🧠