AIMC Topic: Cross-Sectional Studies
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Artificial Intelligence-Assisted Muscular Ultrasonography for Assessing Inflammation and Muscle Mass in Patients at Risk of Malnutrition.
Nutrients
May 9, 2025
BACKGROUND: Malnutrition, influenced by inflammation, is associated with muscle depletion and body composition changes. This study aimed to evaluate muscle mass and quality using Artificial Intelligence (AI)-enhanced ultrasonography in patients with ...
Metabolomic profiles underlying gout flares: a prospective study of people with gout.
RMD open
May 8, 2025
OBJECTIVES: To identify specific metabolomic profiles associated with gout flares in people with gout.
Machine learning of blood haemoglobin and haematocrit levels via smartphone conjunctiva photography in Kenyan pregnant women: a clinical study protocol.
BMJ open
May 8, 2025
INTRODUCTION: Anaemia during pregnancy is a widespread health burden globally, especially in low- and middle-income countries, posing a serious risk to both maternal and neonatal health. The primary challenge is that anaemia is frequently undetected ...
Machine learning-based predictive modelling of mental health in Rwandan Youth.
Scientific reports
May 8, 2025
Globally, mental disorders are a significant burden, particularly in low- and middle-income countries, with high prevalence in Rwanda, especially among survivors of the 1994 genocide against Tutsi. Machine learning offers promise in predicting mental...
Validation of artificial intelligence algorithm LuxIA for screening of diabetic retinopathy from a single 45° retinal colour fundus images: the CARDS study.
BMJ open ophthalmology
May 8, 2025
OBJECTIVE: This study validated the artificial intelligence (AI)-based algorithm LuxIA for screening more-than-mild diabetic retinopathy (mtmDR) from a single 45° colour fundus image of patients with diabetes mellitus (DM, type 1 or type 2) in Spain....
Evaluating the impact of AI-generated educational content on patient understanding and anxiety in endodontics and restorative dentistry: a comparative study.
BMC oral health
May 7, 2025
BACKGROUND: Effective patient education is critical in enhancing treatment outcomes and reducing anxiety in dental procedures. This study compares the effectiveness of AI-generated educational materials with traditional methods in improving patient c...
Novel Artificial Intelligence-Based Quantification of Anterior Chamber Inflammation Using Vision Transformers.
Translational vision science & technology
May 1, 2025
PURPOSE: Quantitative assessment of inflammation is critical for the accurate diagnosis and effective management of uveitis. This study aims to introduce a novel three-dimensional vision transformer approach using anterior segment optical coherence t...
Expectations of healthcare AI and the role of trust: understanding patient views on how AI will impact cost, access, and patient-provider relationships.
Journal of the American Medical Informatics Association : JAMIA
May 1, 2025
OBJECTIVES: Although efforts to effectively govern AI continue to develop, relatively little work has been done to systematically measure and include patient perspectives or expectations of AI in governance. This analysis is designed to understand pa...
Prediction of hypertension and diabetes in twin pregnancy using machine learning model based on characteristics at first prenatal visit: national registry study.
Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
May 1, 2025
OBJECTIVE: To develop a prediction model for hypertensive disorders of pregnancy (HDP) and gestational diabetes mellitus (GDM) in twin pregnancy using characteristics obtained at the first prenatal visit.
Determinants of ascending aortic morphology: cross-sectional deep learning-based analysis on 25 073 non-contrast-enhanced NAKO MRI studies.
European heart journal. Cardiovascular Imaging
Apr 30, 2025
AIMS: Understanding determinants of thoracic aortic morphology is crucial for precise diagnostics and therapeutic approaches. This study aimed to automatically characterize ascending aortic morphology based on 3D non-contrast-enhanced magnetic resona...
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