Artificial Intelligence Medical Compendium

PURPOSE: While data-driven motion correction (DDMC) techniques have proven to enhance the visibility of lesions affected by motion, their impact on overall detectability remains unclear. This study investigates whether DDMC improves lesion detectabil... read more 

Semi-supervised medical image segmentation techniques have demonstrated significant potential and effectiveness in clinical diagnosis. The prevailing approaches using the mean-teacher (MT) framework achieve promising image segmentation results. Howev... read more 

The study of fungal genetics has undergone transformative advancements in recent decades, profoundly reshaping our understanding of fungal diversity, evolution, and pathogenesis. This review synthesizes cutting-edge molecular techniques revolutionizi... read more 

Excess reactive oxygen species leading to oxidative stress has been identified as a significant factor in cardiovascular disease. However, the molecular mechanisms of oxidative stress-related genes in thoracic aortic aneurysm have not been thoroughly... read more 

The critical period for visual function and ocular structure development occurs from 0 to 6 years of age, making standardized eye care and vision screening services during this stage vital for improving lifelong visual outcomes and quality of life. I... read more 

. Sleep apnea is a common sleep disorder associated with severe health risks, necessitating accurate and efficient detection methods.. This study proposes ModelS4Apnea, a deep learning framework for sleep apnea detection from electrocardiogram (ECG) ... read more 

. Limited-angle dual-energy (DE) cone-beam CT (CBCT) is considered as a potential solution to achieve fast and low-dose DE imaging on current CBCT scanners without hardware modification. However, its clinical implementations are hindered by the chall... read more 

OBJECTIVES: There is an application for artificial intelligence (AI) to augment medical education. The aim of this study was to incorporate AI-powered cameras to quantify the learning curve and performance metrics associated with external ventricular... read more 

We present SurfDist, a convolutional neural network architecture for three-dimensional volumetric instance segmentation. SurfDist enables prediction of instances represented as closed surfaces composed of smooth parametric surface patches, specific... read more 

Smart buildings generate vast streams of sensor and control data, but facility managers often lack clear explanations for anomalous energy usage. We propose InsightBuild, a two-stage framework that integrates causality analysis with a fine-tuned la... read more