AIMC Topic: Cross-Sectional Studies

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Application of the deep learning algorithm in nutrition research - using serum pyridoxal 5'-phosphate as an example.

Nutrition journal
BACKGROUND: Multivariable linear regression (MLR) models were previously used to predict serum pyridoxal 5'-phosphate (PLP) concentration, the active coenzyme form of vitamin B6, but with low predictability. We developed a deep learning algorithm (DL...

Sample Size Calculation for Clinical Trials of Medical Decision Support Systems with Binary Outcome.

Sovremennye tekhnologii v meditsine
Currently, software products for use in medicine are actively developed. Among them, the dominant share belongs to clinical decision support systems (CDSS), which can be intelligent (based on mathematical models obtained by machine learning methods o...

Intestinal fibrosis classification in patients with Crohn's disease using CT enterography-based deep learning: comparisons with radiomics and radiologists.

European radiology
OBJECTIVES: Accurate evaluation of bowel fibrosis in patients with Crohn's disease (CD) remains challenging. Computed tomography enterography (CTE)-based radiomics enables the assessment of bowel fibrosis; however, it has some deficiencies. We aimed ...

Digital Health Profile of South Korea: A Cross Sectional Study.

International journal of environmental research and public health
(1) Backgroud: For future national digital healthcare policy development, it is vital to collect baseline data on the infrastructure and services of medical institutions' information and communication technology (ICT). To assess the state of medical ...

Predicting risk of overweight or obesity in Chinese preschool-aged children using artificial intelligence techniques.

Endocrine
OBJECTIVES: We adopted the machine-learning algorithms and deep-learning sequential model to determine and optimize most important factors for overweight and obesity in Chinese preschool-aged children.

Assessing the potentiality of algorithms and artificial intelligence adoption to disrupt patient primary care with a safer and faster medication management: a systematic review protocol.

BMJ open
INTRODUCTION: In primary care, almost 75% of outpatient visits by family doctors and general practitioners involve continuation or initiation of drug therapy. Due to the enormous amount of drugs used by outpatients in unmonitored situations, the pote...

Glaucoma diagnosis using multi-feature analysis and a deep learning technique.

Scientific reports
In this study, we aimed to facilitate the current diagnostic assessment of glaucoma by analyzing multiple features and introducing a new cross-sectional optic nerve head (ONH) feature from optical coherence tomography (OCT) images. The data (n = 100 ...

Current imaging of PE and emerging techniques: is there a role for artificial intelligence?

Clinical imaging
Acute pulmonary embolism (PE) is a critical, potentially life-threatening finding on contrast-enhanced cross-sectional chest imaging. Timely and accurate diagnosis of thrombus acuity and extent directly influences patient management, and outcomes. Te...

Use of artificial intelligence in activating the role of Saudi universities in joint scientific research between university teachers and students.

PloS one
Scientific research in Saudi Arabia's universities has undergone significant changes in recent years with the speed of higher education expansion and the opening of new universities. Artificial intelligence (AI) can be applied to existing data analys...