AIMC Topic: Cross-Sectional Studies
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Dentists' perception and use of AI and robotics in the care of persons with disabilities.
Scientific reports
Jul 29, 2025
Despite the growing role of AI and robotics in healthcare, little is known about their integration into dental care for persons with disabilities (PWDs) in Saudi Arabia. This study aimed to assess dentists' perceptions and attitudes towards and use o...
The application and predictive value of the weight-adjusted-waist index in BC prevalence assessment: a comprehensive statistical and machine learning analysis using NHANES data.
BMC cancer
Jul 29, 2025
BACKGROUND: Obesity is a known risk factor for breast cancer (BC), but conventional metrics such as body mass index (BMI) may insufficiently capture central adiposity. The weight-adjusted waist index (WWI) has emerged as a potentially superior anthro...
Identification of age-specific risk factors for hyperuricemia: a machine learning-driven stratified analysis in health examination cohorts.
BMC medical informatics and decision making
Jul 28, 2025
BACKGROUND: Hyperuricemia (HUA) as a global public health challenge, although its overall epidemiological characteristics have been widely reported, its age-specific risk pattern remains controversial. This study aims to reveal the risk factors of HU...
Precision nutrition in epigenetic aging: SHAP-optimized machine learning identifies omega-3 constituent-specific associations with aging biomarkers.
Biogerontology
Jul 24, 2025
This cross-sectional investigation seeks to examine the association between dietary omega-3 fatty acids (including α-linolenic acid [ALA], eicosapentaenoic acid [EPA], and docosahexaenoic acid [DHA]) and biomarkers of cellular aging, specifically DNA...
An illustration of multi-class roc analysis for predicting internet addiction among university students.
PloS one
Jul 21, 2025
The internet is one of the essential tools today, and its impact is particularly felt among university students. Internet addiction (IA) has become a serious public health issue worldwide. This multi-class classification study aimed to identify the p...
Application of machine learning algorithms and SHAP explanations to predict fertility preference among reproductive women in Somalia.
Scientific reports
Jul 20, 2025
Fertility preferences significantly influence population dynamics and reproductive health outcomes, particularly in low-resource settings, such as Somalia, where high fertility rates and limited healthcare infrastructure pose significant challenges. ...
Deep Learning-Based Precision Cropping of Eye Regions in Strabismus Photographs: Algorithm Development and Validation Study for Workflow Optimization.
Journal of medical Internet research
Jul 17, 2025
BACKGROUND: Traditional ocular gaze photograph preprocessing, relying on manual cropping and head tilt correction, is time-consuming and inconsistent, limiting artificial intelligence (AI) model development and clinical application.
Knowledge and perception of artificial lntelligence education among undergraduate healthcare students.
BMC medical education
Jul 16, 2025
BACKGROUND: Artificial intelligence (AI) is gaining recognition for its ability to enhance patient outcomes in healthcare. Therefore, integrating AI into the undergraduate curriculum is essential to equip students with foundational knowledge before g...
Characterising corneal changes in aniridia-related keratopathy using in vivo confocal microscopy and a self-supervised AI model.
BMJ open ophthalmology
Jul 16, 2025
PURPOSE: To investigate whether corneal changes observed via in vivo confocal microscopy (IVCM) in patients with aniridia-related keratopathy (ARK) reflect clinical severity.
Feasibility of Digitally Identifying and Minimizing Stressors in Palliative Care Workplaces by Measuring Stress Continuously for Nurses Through Wearable Sensors (DiPa): Protocol for a Prospective Cross-Sectional Study.
JMIR research protocols
Jul 16, 2025
BACKGROUND: Nursing in palliative medicine combines primary patient care with the special challenges of this medical field (eg, handling the processes of dying, grief, and death). These cause high stress levels and burden on the nursing staff, result...
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