Artificial Intelligence Medical Compendium

This study proposes a Heterogeneous Hybrid Machine Learning Model (HHMM) for the early detection of cardiac arrest in newborns in intensive care. The model combines three classifiers through stacking: XGBoost (XGB), Random Forest (RF), and Multilayer... read more 

Tooth and endodontic disease pose important challenges, and traditional therapies are dependent upon mechanical restoration rather than biological regeneration. Progress in tissue engineering, biofabrication, and biomaterials science is increasingly ... read more 

KEY POINTS: Artificial intelligence models effectively generalized across studies and animal models and reduced translational gaps when applied to human biopsies. Artificial intelligence assistance reduced study evaluation turnaround times by up to 9... read more 

OBJECTIVE: To develop a machine learning (ML) model to identify fiducial points on canine ECGs to localize right-sided accessory pathways as posterior or anterior during ventricular preexcitation (VPE). METHODS: ECG recordings with VPE and documented... read more 

Artificial intelligence may be able to improve the efficiency, accuracy and predictiveness of mental health assessments, and public perspectives are crucial to ensuring these tools are implemented with fairness and accountability. We developed an art... read more 

Identifying essential human microRNAs (miRNAs) remains challenging due to scarce experimental labels. Traditional domain adaptation methods for cross-species knowledge transfer have significant limitations in capturing intricate data features and han... read more 

Fine-tuned REINVENT generative model integrated with structure- and ligand-based computational approaches was applied to identify novel EZH2 inhibitors. The generated molecules closely reproduced the structural and physicochemical property distributi... read more 

PURPOSE: Colorectal cancer is an aggressive malignancy characterized by significant drug resistance and a complex tumor microenvironment. Nano-dihydroartemisinin liposomes demonstrate significant potential in inhibiting tumor growth and survival by t... read more 

The recognition of steak doneness during cooking poses challenges for consumers. In this study, the framework of deep learning-driven non-destructive steak doneness assessment during cooking was constructed. In addition, physicochemical properties we... read more 

Multi-hop link prediction in knowledge graphs remains a challenging task, particularly in modeling complex reasoning paths and handling uncertainty. Existing approaches often fail to capture the interactive dependencies between the reasoning context ... read more