Artificial Intelligence Medical Compendium

PURPOSE: Fat fraction (FF) quantification in individual muscles using quantitative MRI is of major importance for monitoring disease progression and assessing disease severity in neuromuscular diseases. Undersampling of MRI acquisitions is commonly u... read more 

BACKGROUND: We aimed to develop and validate a radiomics-based machine learning nomogram using multiparametric magnetic resonance imaging to preoperatively predict substantial lymphovascular space invasion in patients with endometrial cancer. METHODS... read more 

The emergence of big data and analytic approaches initiated research efforts to characterise different subtypes of allergic diseases, including tracking disease progression and identifying patterns that may offer insight into their development and pr... read more 

BACKGROUND: The use of artificial intelligence (AI) in medicine has increased dramatically. Its use will expand in cardiothoracic (CT) surgery and alter current practice. This study surveyed CT surgeons to gain insight into their beliefs, hopes, and ... read more 

BACKGROUND: World Health Organisation (WHO) in 2024 identified that approximately one in 100 children globally has autism spectrum disorder (ASD). ASD is a collection of neurodevelopmental disorders that impact a person's ability to socially interact... read more 

With the advancement of technology and the focus on health, biosensors have undergone a shift in focus from breakthroughs in a single area to an emphasis on improving integrated performance. Herein, aiming at two principal aspects of signal transduct... read more 

INTRODUCTION: Leukemia is one of the most prevalent cancers worldwide, and early detection is critical for effective treatment. Microarray data is a key tool in this process, given the vast number of genes involved, which makes the analysis complex a... read more 

Drug-target interaction (DTI) prediction plays a crucial role in drug discovery and repurposing by efficiently and accurately identifying potential therapeutic targets. Existing methods face challenges in capturing high-order semantic relationships i... read more 

Multi-modal neuroimaging techniques are widely employed for the accurate diagnosis of Alzheimer's Disease (AD). Existing fusion methods typically focus on capturing semantic correlations between modalities through feature-level interactions. However,... read more 

BACKGROUND: Assessing skills in simulated settings is resource-intensive and lacks validated metrics. Advances in AI offer the potential for automated competence assessment, addressing these limitations. This study aimed to develop and validate a mac... read more