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Automatic detection of developmental stages of molar teeth with deep learning.

BMC oral health
BACKGROUND: The aim was to fully automate molar teeth developmental staging and to comprehensively analyze a wide range of deep learning models' performances for molar tooth germ detection on panoramic radiographs.

Identification of biomarkers associated with M1 macrophages in the ST-segment elevation myocardial infarction through bioinformatics and machine learning approaches.

Scientific reports
ST-segment elevation myocardial infarction (STEMI) is considered a critical cardiac condition with a poor prognosis. Shortly after STEMI occurs, the increased number of circulating leukocytes including macrophages can lead to the accumulation of more...

AI-based detection and classification of anomalous aortic origin of coronary arteries using coronary CT angiography images.

Nature communications
Anomalous aortic origin of the coronary artery (AAOCA) is a rare cardiac condition that can lead to ischemia or sudden cardiac death, yet it is often overlooked or falsely classified in routine coronary CT angiography (CCTA). Here, we developed, vali...

Emerging Models of Care Using IT in Long-Term/Post-Acute Care: A Comparative Analysis of Human and AI-Driven Qualitative Insights.

Journal of gerontological nursing
PURPOSE: As the global population ages, long-term/post-acute care (LTPAC) systems face challenges in ensuring quality care for older adults with complex medical needs. Using health information technology (IT) is a promising strategy to address these ...

Gender Differences in Predicting Metabolic Syndrome Among Hospital Employees Using Machine Learning Models: A Population-Based Study.

The journal of nursing research : JNR
BACKGROUND: Metabolic syndrome (MetS) is a complex condition that captures several markers of dysregulation, including obesity, elevated blood glucose levels, dyslipidemia and hypertension. Using an approach to early prediction of MetS risk in hospit...

Adoption of Large Language Model AI Tools in Everyday Tasks: Multisite Cross-Sectional Qualitative Study of Chinese Hospital Administrators.

Journal of medical Internet research
BACKGROUND: Large language model (LLM) artificial intelligence (AI) tools have the potential to streamline health care administration by enhancing efficiency in document drafting, resource allocation, and communication tasks. Despite this potential, ...

Artificial Intelligence-Enhanced Perfusion Scoring Improves the Diagnostic Accuracy of Myocardial Perfusion Imaging.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
We previously demonstrated that a deep learning (DL) model of myocardial perfusion SPECT imaging improved accuracy for detection of obstructive coronary artery disease (CAD). We aimed to improve the clinical translatability of this artificial intelli...

Development, validation, and clinical evaluation of a machine-learning based model for diagnosing early infection after cardiovascular surgery (DEICS): a multi-center cohort study.

International journal of surgery (London, England)
BACKGROUND: This study addresses the critical need for timely and accurate diagnosis of early postoperative infection (EPI) following cardiac surgery. EPI significantly impacts patient outcomes and healthcare costs, making its early detection vital.

Construction of an artificially intelligent model for accurate detection of HCC by integrating clinical, radiological, and peripheral immunological features.

International journal of surgery (London, England)
BACKGROUND: Integrating comprehensive information on hepatocellular carcinoma (HCC) is essential to improve its early detection. We aimed to develop a model with multimodal features (MMF) using artificial intelligence (AI) approaches to enhance the p...

Machine Learning-Guided Fluid Resuscitation for Acute Pancreatitis Improves Outcomes.

Clinical and translational gastroenterology
INTRODUCTION: Ariel Dynamic Acute Pancreatitis Tracker (ADAPT) is an artificial intelligence tool using mathematical algorithms to predict severity and manage fluid resuscitation needs based on the physiologic parameters of individual patients. Our a...