Artificial Intelligence Medical Compendium

PURPOSE: To evaluate whether quantitative diffusion-weighted magnetic resonance imaging derived apparent diffusion coefficient (ADC) parametric values of nodal disease can identify nodal recurrence (NR) risk and distinguish patients with human papill... read more 

PURPOSE: To develop and validate DeepMocor, a deep learning-based method for motion-compensated 4-dimensional magnetic resonance fingerprinting (4D-MRF) reconstruction to accelerate conventional 4D-MRF reconstruction, enabling more efficient clinical... read more 

BACKGROUND: Bipolar II disorder (BD-II) can progress to bipolar I disorder (BD-I), carrying profound psychosocial consequences. However, limited research has investigated the transition from BD-II to BD-I. Our study uses machine learning to predict t... read more 

INTRODUCTION: Optimal ovarian stimulation (OS) selection is critical for IVF success, but expert-based decisions often lack consistency in outcomes, cost-efficiency, and personalization, highlighting the need for more individualized and data-driven a... read more 

The nitrogen metabolism genes and associated microorganisms in polar oceans and hydrothermal vents remain insufficiently studied. In this study, metagenomic data were analyzed to characterize the geographical and biological features of 16 key nitroge... read more 

Microplastics pollution has become the second-largest global environmental issue. Therefore, accurate and rapid quantification of microplastics is of great significance. This study proposed a novel quantitative approach that integrated near-infrared ... read more 

BACKGROUND: Brain-Computer Interfaces (BCI) are a type of life-altering neurotechnology, but their inherent complexity poses significant challenges to patient education. Large Language Models (LLMs), such as ChatGPT and Gemini, offer new possibilitie... read more 

OBJECTIVES: This study aims to determine reliable reference intervals (RIs) for total cortisol (TC) in adults considering the effects of both age and blood collection time, using indirect methods and machine learning approaches. METHODS: Serum TC res... read more 

INTRODUCTION: Large language models (LLMs) are increasingly used in healthcare, including urogynecology, where stigma may limit open discussion. LLM-based chat platforms may provide a less intimidating and more accessible way for patients to obtain i... read more 

OBJECTIVES: This study aimed to develop and validate machine-learning (ML) models that integrate ultrasonic radiofrequency (RF) time-series signals with gray-scale image features for the preoperative differentiation of breast lesions classified as ca... read more