Artificial Intelligence Medical Compendium

BACKGROUND: Vacuum-assisted headspace solid-phase microextraction (Vac-HSSPME) is increasingly recognized as a powerful and environmentally friendly technique for extracting volatile and semi-volatile compounds from complex food matrices. While conve... read more 

This study developed a deep learning-based method for high resolution bathymetry prediction in the Philippine sea, aiming to improve the accuracy of seafloor depth estimation using multi-source marine geodetic data. The method integrates geographic c... read more 

Urine cytology is widely used for detecting urothelial carcinoma (UC), though its performance is constrained by limited sensitivity and substantial interobserver variability. An interpretable machine learning framework was developed to classify uroth... read more 

OBJECTIVES: The increasing digitisation of healthcare data and the rapid development of Artificial Intelligence (AI) pave the way for innovative strategies for infectious disease management. This study aimed to systematically retrieve and summarize c... read more 

Chocolate is a versatile product with flavours ranging from sweet to bitter. Cocoa, from a C3-photosynthetic plant, is its main raw material, while sugar from sugarcane (C4-metabolism) is commonly used. We analyzed the δ13C and δ15N composition of Br... read more 

BACKGROUND: Hepatocellular carcinoma (HCC) remains a therapeutic challenge due to high post-resection recurrence rates and heterogeneous outcomes. We developed and validated a digital pathology-based prognostic model combining pathomics signatures wi... read more 

Resting-state functional MRI (rs-fMRI) is widely used for diagnosing and analyzing brain disorders. However, existing fMRI studies have shown that learning-based approaches depend heavily on labeled training data, which is difficult to obtain due to ... read more 

BACKGROUND: ICU patients often suffer from critical and complex condition, and multiple potential risks should be monitored to provide them comprehensive care. However, no study proposes continual learning (CL) model that can effectively solve multip... read more 

Unsupervised domain adaptive object detection (UDAOD) aims to effectively apply the detector trained on a labeled (source domain) and an unlabeled (target domain) dataset to the target domain. The mean teacher framework has demonstrated good applicab... read more 

Lung cancer's metastatic propensity and recurrence prevalence necessitate innovative immunotherapy strategies beyond conventional single-mode regulation. We engineered low-capacity turmeric-derived extracellular vesicles (TEVs) that integrated with z... read more