AIMC Topic: Prospective Studies

Clear Filters Showing 731 to 740 of 2510 articles

Enhancing Nigrosome-1 Sign Identification via Interpretable AI using True Susceptibility Weighted Imaging.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Nigrosome 1 (N1), the largest nigrosome region in the ventrolateral area of the substantia nigra pars compacta, is identifiable by the "N1 sign" in long echo time gradient echo MRI. The N1 sign's absence is a vital Parkinson's disease (PD...

Use of a Novel Artificial Intelligence System Leads to the Detection of Significantly Higher Number of Adenomas During Screening and Surveillance Colonoscopy: Results From a Large, Prospective, US Multicenter, Randomized Clinical Trial.

The American journal of gastroenterology
INTRODUCTION: Adenoma per colonoscopy (APC) has recently been proposed as a quality measure for colonoscopy. We evaluated the impact of a novel artificial intelligence (AI) system, compared with standard high-definition colonoscopy, for APC measureme...

Percutaneous nephrolithotomy guided by 5G-powered robot-assisted teleultrasound diagnosis system: first clinical experience with a novel tele-assistance approach (IDEAL stage 1).

BMC urology
BACKGROUND: To demonstrate the technical feasibility of percutaneous nephrolithotomy (PCNL) guided by 5G-powered robot-assisted teleultrasound diagnosis system (RTDS) in a complex kidney-stone (CKS) cohort and present our preliminary outcomes. PCNL i...

Validation of reliability, repeatability and consistency of three-dimensional choroidal vascular index.

Scientific reports
This study aimed to investigate the reliability, repeatability and consistency of choroidal vascularity index (CVI) measurements provided by an artificial intelligence-based software in swept-source optical coherence tomography (SS-OCT) in normal sub...

A Machine Learning Approach to Predict Post-stroke Fatigue. The Nor-COAST study.

Archives of physical medicine and rehabilitation
OBJECTIVE: This study aimed to predict fatigue 18 months post-stroke by utilizing comprehensive data from the acute and sub-acute phases after stroke in a machine-learning set-up.

ChatGPT in Medical Education: A Precursor for Automation Bias?

JMIR medical education
Artificial intelligence (AI) in health care has the promise of providing accurate and efficient results. However, AI can also be a black box, where the logic behind its results is nonrational. There are concerns if these questionable results are used...

Prediction of preeclampsia from retinal fundus images via deep learning in singleton pregnancies: a prospective cohort study.

Journal of hypertension
INTRODUCTION: Early prediction of preeclampsia (PE) is of universal importance in controlling the disease process. Our study aimed to assess the feasibility of using retinal fundus images to predict preeclampsia via deep learning in singleton pregnan...

Predictive Minisci late stage functionalization with transfer learning.

Nature communications
Structural diversification of lead molecules is a key component of drug discovery to explore chemical space. Late-stage functionalizations (LSFs) are versatile methodologies capable of installing functional handles on richly decorated intermediates t...

Analysis of KangDuo-SR-1500 and KangDuo-SR-2000 robotic partial nephrectomy from an operative and ergonomic perspective: a prospective controlled study in porcine models.

Journal of robotic surgery
The objective of this study is to explore the safety and effectiveness of two new models of KangDuo surgical robots for partial nephrectomy in porcine models, and evaluate the ergonomic characteristics from both subjective and objective perspectives....

A Deep-Learning Model for Diagnosing Fresh Vertebral Fractures on Magnetic Resonance Images.

World neurosurgery
BACKGROUND: The accurate diagnosis of fresh vertebral fractures (VFs) was critical to optimizing treatment outcomes. Existing studies, however, demonstrated insufficient accuracy, sensitivity, and specificity in detecting fresh fractures using magnet...