Artificial Intelligence Medical Compendium

Breast cancer continues to be a major global health concern, particularly for women, despite improvements in early detection and treatment strategies. Traditional tissue biopsies have long been the foundation of diagnosis and treatment planning; howe... read more 

Selenium (Se) biofortification in crops has emerged as a promising strategy to address global Se deficiencies and enhance both agricultural productivity and human health. Increasing the Se content of crops through biofortification improves their resi... read more 

The deep integration of artificial intelligence (AI) with research on food-derived peptides (FDPs) signifies a paradigm shift towards intelligent precision design and functional optimization in developing FDP products. Random forests, convolutional n... read more 

Accurate and early diagnosis of Alzheimer's Disease (AD) is crucial for timely interventions and treatment advancement. Functional Magnetic Resonance Imaging (fMRI), measuring brain blood-oxygen level changes over time, is a powerful AD-diagnosis too... read more 

Long-term continuous monitoring of volatile organic compounds (VOCs) is pivotal for climate change research, air quality assessment, pollution source identification, and public health early warning systems. Prolonged VOC monitoring is routinely imple... read more 

Owing to the unsound cold chain system in China, chilled beef's quality would be affected by psychrophilic bacteria, resulting in quality deterioration and corruption, which leads to food safety problems. In this study, the growth of Pseudomonas and ... read more 

Aspergillus niger is an important species in the fungal community of many foods and is one of the most significant microorganisms used in biotechnology. Some A. niger strains are capable of producing fumonisin B (FB) under certain conditions, but lit... read more 

BACKGROUND: Traditional Chinese culture makes death a sensitive and taboo topic, leading patients and family members to refuse to choose palliative care. read more 

OBJECTIVES: To develop a convolutional neural network (CNN) model to diagnose thyroid cartilage invasion by laryngeal and hypopharyngeal cancers observed on computed tomography (CT) images and evaluate the model's diagnostic performance. read more 

OBJECTIVE: To assess the clinical value of the deep learning image reconstruction (DLIR) algorithm compared with conventional adaptive statistical iterative reconstruction-Veo (ASiR-V) in image quality, diagnostic confidence, and intestinal lesion de... read more