AIMC Topic:
Cross-Sectional Studies
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Machine Learning Models for Predicting Cycloplegic Refractive Error and Myopia Status Based on Non-Cycloplegic Data in Chinese Students.
Translational vision science & technology
Aug 1, 2024
PURPOSE: To develop and validate machine learning (ML) models for predicting cycloplegic refractive error and myopia status using noncycloplegic refractive error and biometric data.
Artificial Intelligence Readiness, Perceptions, and Educational Needs Among Dental Students: A Cross-Sectional Study.
Clinical and experimental dental research
Aug 1, 2024
OBJECTIVES: With Artificial Intelligence (AI) profoundly affecting education, ensuring that students in health disciplines are ready to embrace AI is essential for their future workforce integration. This study aims to explore dental students' readin...
[Constructing a cataplexy face prediction model for narcolepsy type 1 based on ResNet-18].
Zhonghua yi xue za zhi
Jul 16, 2024
To establish a prediction model for the identifying of cataplexy facial features based on clinical shooting videos by using a deep learning image recognition network ResNet-18. A cross-sectional study. Twenty-five narcolepsy type 1 patients who wer...
Assessment of Knowledge, Practice, Perception, and Expectations of Artificial Intelligence in Medical Care among Staff of a Tertiary Hospital.
Ethiopian journal of health sciences
Jul 1, 2024
BACKGROUND: Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. AI technology has wide applications in biomedicine and has real practical benefits in many medical applications. The ...
Multi-Plexus Nonperfusion Area Segmentation in Widefield OCT Angiography Using a Deep Convolutional Neural Network.
Translational vision science & technology
Jul 1, 2024
PURPOSE: To train and validate a convolutional neural network to segment nonperfusion areas (NPAs) in multiple retinal vascular plexuses on widefield optical coherence tomography angiography (OCTA).
Leveraging multivariate analysis and adjusted mutual information to improve stroke prediction and interpretability.
Neurosciences (Riyadh, Saudi Arabia)
Jul 1, 2024
OBJECTIVES: To develop a machine learning model to accurately predict stroke risk based on demographic and clinical data. It also sought to identify the most significant stroke risk factors and determine the optimal machine learning algorithm for str...
Evaluating the readiness of healthcare administration students to utilize AI for sustainable leadership: a survey study.
Journal of health organization and management
Jun 11, 2024
PURPOSE: This paper explores how healthcare administration students perceive the integration of Artificial Intelligence (AI) in healthcare leadership, mainly focusing on the sustainability aspects involved. It aims to identify gaps in current educati...
Deep Learning-Based Assessment of Built Environment From Satellite Images and Cardiometabolic Disease Prevalence.
JAMA cardiology
Jun 1, 2024
IMPORTANCE: Built environment plays an important role in development of cardiovascular disease. Large scale, pragmatic evaluation of built environment has been limited owing to scarce data and inconsistent data quality.
Application of improved glomerular filtration rate estimation by a neural network model in patients with neurogenic lower urinary tract dysfunction.
Clinical nephrology
Jun 1, 2024
BACKGROUND: Previous studies have indicated that creatinine (Cr)-based glomerular filtration rate (GFR) estimating equations - including the new Chronic Kidney Disease Epidemiology creatinine (CKD-EPI) equation without race and the estimated glomerul...
Current State of Dermatology Mobile Applications With Artificial Intelligence Features.
JAMA dermatology
Jun 1, 2024
IMPORTANCE: With advancements in mobile technology and artificial intelligence (AI) methods, there has been a substantial surge in the availability of direct-to-consumer mobile applications (apps) claiming to aid in the assessment and management of d...
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