BACKGROUND: To reduce the high incidence and mortality of gastric cancer (GC), we aimed to develop deep learning-based models to assist in predicting the diagnosis and overall survival (OS) of GC patients using pathological images.
BACKGROUND: Laboratory testing is routinely used to assay blood biomarkers to provide information on physiologic state beyond what clinicians can evaluate from interpreting medical imaging. We hypothesized that deep learning interpretation of echocar...
BACKGROUND: Respiratory virus infections are significant causes of morbidity and mortality, and may induce host metabolite alterations by infecting respiratory epithelial cells. We investigated the use of liquid chromatography quadrupole time-of-flig...
BACKGROUND: Induction chemotherapy (ICT) plus concurrent chemoradiotherapy (CCRT) and CCRT alone were the optional treatment regimens in locoregionally advanced nasopharyngeal carcinoma (NPC) patients. Currently, the choice of them remains equivocal ...
BACKGROUND: Machine learning sustains successful application to many diagnostic and prognostic problems in computational histopathology. Yet, few efforts have been made to model gene expression from histopathology. This study proposes a methodology w...
Artificial Intelligence (AI) can potentially impact many aspects of human health, from basic research discovery to individual health assessment. It is critical that these advances in technology broadly benefit diverse populations from around the worl...
BACKGROUND: artificial intelligence (AI) for cellular phenotyping diagnosis of nasal polyps by whole-slide imaging (WSI) is lacking. We aim to establish an AI chronic rhinosinusitis evaluation platform 2.0 (AICEP 2.0) to obtain the proportion of infl...
BACKGROUND: Assistive automatic seizure detection can empower human annotators to shorten patient monitoring data review times. We present a proof-of-concept for a seizure detection system that is sensitive, automated, patient-specific, and tunable t...