Artificial Intelligence Medical Compendium

Traditional risk assessment methods frequently overlook the dynamic adsorption-desorption processes of cadmium (Cd) at solid-liquid interfaces and underutilize the integrative predictive potential of easily measurable soil indicators. This study cons... read more 

OBJECTIVE: Low-grade epilepsy-associated neuroepithelial tumors (LEATs) often cause drug-resistant epilepsy. Despite complete resection of these lesions, approximately 20% of patients continue to experience suboptimal seizure control. This study aims... read more 

In recent years, optimization-inspired networks that integrate optimization theory into deep neural networks (DNNs) have achieved remarkable success in image compressed sensing (ICS). However, current DNN-based ICS methods face two key challenges: (1... read more 

BACKGROUND AND OBJECTIVE: Medullary thyroid carcinoma (MTC) is an aggressive malignancy driven predominantly by activating mutations in the RET proto-oncogene. Conventional genotyping using polymerase chain reaction (PCR) or next-generation sequencin... read more 

Phosphorus (P) is a critical biogenic element driving aquatic productivity and eutrophication in freshwater systems. However, monitoring total phosphorus (TP) in shallow, dynamic lakes remain challenging due to its pronounced spatiotemporal variabili... read more 

OBJECTIVE: Artificial intelligence (AI) methods have been widely applied in gait analysis, yet quantitative comparisons across models and their input-output specifications remain limited. This study aims to systematically review and synthesize the ex... read more 

PURPOSE: To evaluate the measurement variability and reproducibility of prone imaging with quantitative computed tomography (QCT) compared with supine imaging with QCT in patients with interstitial lung disease (ILD). METHODS: Data from patients who ... read more 

Near-infrared spectroscopy (NIRS) prediction models offer an expeditious method for assessing the nutritional value of feedstuffs, however, developing high-performance models using undried and unground samples remains challenging. This study aimed to... read more 

Dynamic magnetic resonance imaging (MRI) requires accurate reconstruction from undersampled k-space data to achieve high temporal resolution within clinically acceptable scan times. Deep unrolling architectures have recently emerged as effective solu... read more 

Visual prioritization has traditionally been explained through stimulus-driven salience. However, emerging evidence from neuroscience and computational modeling supports a predictive processing framework, in which gaze behavior reflects active infere... read more