Artificial Intelligence Medical Compendium

Brain age is an emerging concept that reflects complex, time-dependent changes in brain structure, identifying departures from expected neurodevelopmental patterns. In the developing brain, accurate MRI-based age estimation is a quantitative biomarke... read more 

BACKGROUND: Graves' disease (GD), a leading cause of hyperthyroidism, exhibits heterogeneous responses to iodine-131 therapy, underscoring the need for accurate predictive tools. While pertechnetate ( 99m TcO 4- ) thyroid scintigraphy provides essent... read more 

Tooth whitening has attracted considerable attention as it can enhance appearance and improve oral health. Nanocatalysts with peroxidase-like activity can catalyze H2O2 to generate reactive oxygen species (ROS), a process known as chemodynamic therap... read more 

Breast cancer remains a significant global health concern, emphasizing the need for advanced and accurate diagnostic tools. This research paper focuses on the application of a Transfer Learning model for the detection of breast cancer in mammography ... read more 

Digitalization opens up a wide range of didactic and organizational possibilities for examinations in medical curricula. This article provides a systematic overview of the potential, challenges, and current implementation scenarios of digital examina... read more 

Liver tumor diagnosis relies heavily on imaging, and the liver imaging reporting and data system (LI-RADS) provides a structured framework for evaluating hepatocellular carcinoma (HCC) and related entities in at-risk populations. Artificial intellige... read more 

PURPOSE: To map the intellectual evolution of pancreatic radiology through a comprehensive bibliometric analysis of the 100 most-cited articles, identifying influential contributors and publications, geographical and institutional patterns, and delin... read more 

OBJECTIVE: To develop a prediction model combining radiomics features from 2D ultrasound (2D-US) and shear wave elastography (SWE) with clinical indicators for assessing significant hepatic fibrosis (S2-4) in autoimmune liver diseases (AILDs). METHOD... read more 

OBJECTIVE: To explore the application value of a combined model automatic segmentation-based radiomics and deep learning, integrated with clinical parameters, in differentiating adrenal lipid-poor adenomas and metastases. METHODS: This study included... read more 

OBJECTIVES: To assess the feasibility and accuracy of using deep learning to generate simulated contrast-enhanced T1-weighted rectal MRI scans from pre-contrast MRI sequences in rectal cancer patients. METHODS: This study included 514 patients with p... read more