AIMC Topic: Middle Aged

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A novel endoscopic artificial intelligence system to assist in the diagnosis of autoimmune gastritis: a multicenter study.

Endoscopy
BACKGROUND:  Autoimmune gastritis (AIG), distinct from Helicobacter pylori-associated atrophic gastritis (HpAG), is underdiagnosed due to limited awareness. This multicenter study aimed to develop a novel endoscopic artificial intelligence (AI) syste...

An explainable machine learning estimated biological age based on morphological parameters of the spine.

GeroScience
Accurately estimating biological age is beneficial for measuring aging and predicting risk. It is widely accepted that the prevalence of spine compression increases significantly with age. However, biological age based on vertebral morphological data...

A new approach to assess post-mortem interval: A machine learning-assisted label-free ATR-FTIR analysis of human vitreous humor.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
A crucial issue in forensics is determining the post-mortem interval (PMI), the time between death and the finding of a body. Despite various methods already employed for its estimation, only approximate values are currently achievable. Vitreous humo...

Clinical evaluation of accelerated diffusion-weighted imaging of rectal cancer using a denoising neural network.

European journal of radiology
BACKGROUND: To evaluate the effectiveness of a deep learning denoising approach to accelerate diffusion-weighted imaging (DWI) and thus improve diagnostic accuracy and image quality in restaging rectal MRI following total neoadjuvant therapy (TNT).

Detection of carotid plaques on panoramic radiographs using deep learning.

Journal of dentistry
OBJECTIVES: Panoramic radiographs (PRs) can reveal an incidental finding of atherosclerosis, or carotid artery calcification (CAC), in 3-15% of examined patients. However, limited training in identification of such calcifications among dental profess...

Comparison of Manual vs Artificial Intelligence-Based Muscle MRI Segmentation for Evaluating Disease Progression in Patients With CMT1A.

Neurology
BACKGROUND AND OBJECTIVES: Intramuscular fat fraction (FF), assessed with quantitative MRI (qMRI), has emerged as one of the few responsive outcome measures in CMT1A patients. The main limitation for its use in future therapeutic trials is the time r...

Use of Deep Learning to Identify Peripheral Arterial Disease Cases From Narrative Clinical Notes.

The Journal of surgical research
INTRODUCTION: Peripheral arterial disease (PAD) is the leading cause of amputation in the United States. Despite affecting 8.5 million Americans and more than 200 million people globally, there are significant gaps in awareness by both patients and p...

Using Atrial Fibrillation Burden Trends and Machine Learning to Predict Near-Term Risk of Cardiovascular Hospitalization.

Circulation. Arrhythmia and electrophysiology
BACKGROUND: Atrial fibrillation is associated with an increased risk of cardiovascular hospitalization (CVH), which may be triggered by changes in daily burden. Machine learning of dynamic trends in atrial fibrillation burden, as measured by insertab...