AIMC Topic: Cross-Sectional Studies
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Risk Assessment and Determination of Factors That Cause the Development of Hyperinsulinemia in School-Age Adolescents.
Medicina (Kaunas, Lithuania)
Dec 22, 2021
: Hyperinsulinemia and insulin resistance are not synonymous; if the risk of developing insulin resistance in adolescents is monitored, they do not necessarily have hyperinsulinemia. It is considered a condition of pre-diabetes and represents a condi...
Proof of concept and development of a couple-based machine learning model to stratify infertile patients with idiopathic infertility.
Scientific reports
Dec 14, 2021
We aimed to develop and evaluate a machine learning model that can stratify infertile/fertile couples on the basis of their bioclinical signature helping the management of couples with unexplained infertility. Fertile and infertile couples were recru...
Needs Assessment Survey Identifying Research Processes Which may be Improved by Automation or Artificial Intelligence: ICU Community Modeling and Artificial Intelligence to Improve Efficiency (ICU-Comma).
Journal of intensive care medicine
Dec 13, 2021
BACKGROUND: Critical care research in Canada is conducted primarily in academically-affiliated intensive care units with established research infrastructure, including research coordinators (RCs). Recently, efforts have been made to engage community ...
A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak, Malaysia.
Computational and mathematical methods in medicine
Dec 7, 2021
This study outlines and developed a multilayer perceptron (MLP) neural network model for adolescent hypertension classification focusing on the use of simple anthropometric and sociodemographic data collected from a cross-sectional research study in ...
Left ventricular systolic dysfunction predicted by artificial intelligence using the electrocardiogram in Chagas disease patients-The SaMi-Trop cohort.
PLoS neglected tropical diseases
Dec 6, 2021
BACKGROUND: Left ventricular systolic dysfunction (LVSD) in Chagas disease (ChD) is relatively common and its treatment using low-cost drugs can improve symptoms and reduce mortality. Recently, an artificial intelligence (AI)-enabled ECG algorithm sh...
The effect of a post-scan processing denoising system on image quality and morphometric analysis.
Journal of neuroradiology = Journal de neuroradiologie
Dec 2, 2021
PURPOSE: MR image quality and subsequent brain morphometric analysis are inevitably affected by noise. The purpose of this study was to evaluate the effectiveness of an artificial intelligence (AI)-based post-scan processing denoising system, intelli...
Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models.
Scientific reports
Dec 2, 2021
In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of r...
Deep neural network for video colonoscopy of ulcerative colitis: a cross-sectional study.
The lancet. Gastroenterology & hepatology
Nov 29, 2021
BACKGROUND: A combination of endoscopic and histological evaluation is important in the management of patients with ulcerative colitis. We aimed to adapt our previous deep neural network system (deep neural ulcerative colitis [DNUC]) to full video co...
A machine learning framework for the evaluation of myocardial rotation in patients with noncompaction cardiomyopathy.
PloS one
Nov 29, 2021
AIMS: Noncompaction cardiomyopathy (NCC) is considered a genetic cardiomyopathy with unknown pathophysiological mechanisms. We propose to evaluate echocardiographic predictors for rigid body rotation (RBR) in NCC using a machine learning (ML) based m...
A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography.
Scientific reports
Nov 26, 2021
As the prevalence of diabetes increases, millions of people need to be screened for diabetic retinopathy (DR). Remarkable advances in technology have made it possible to use artificial intelligence to screen DR from retinal images with high accuracy ...
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