AIMC Topic: Cross-Sectional Studies
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Assessing facial weakness in myasthenia gravis with facial recognition software and deep learning.
Annals of clinical and translational neurology
Jun 9, 2023
OBJECTIVE: Myasthenia gravis (MG) is an autoimmune disease leading to fatigable muscle weakness. Extra-ocular and bulbar muscles are most commonly affected. We aimed to investigate whether facial weakness can be quantified automatically and used for ...
Performance of an automated deep learning algorithm to identify hepatic steatosis within noncontrast computed tomography scans among people with and without HIV.
Pharmacoepidemiology and drug safety
Jun 8, 2023
PURPOSE: Hepatic steatosis (fatty liver disease) affects 25% of the world's population, particularly people with HIV (PWH). Pharmacoepidemiologic studies to identify medications associated with steatosis have not been conducted because methods to eva...
Pain, dynamic postural control, mental health and impact of oral health in individuals with temporomandibular disorder: A cross-sectional study.
Journal of bodywork and movement therapies
May 29, 2023
INTRODUCTION: Some studies claim that functional changes in TMD affect the stomatognathic system (SS) and could contribute to the emergence of pain and changes in postural control.
Deep learning in optical coherence tomography: Where are the gaps?
Clinical & experimental ophthalmology
May 28, 2023
Optical coherence tomography (OCT) is a non-invasive optical imaging modality, which provides rapid, high-resolution and cross-sectional morphology of macular area and optic nerve head for diagnosis and managing of different eye diseases. However, in...
Prediction of Anemia From Cerebral Venous Sinus Attenuation on Deep-Learning Reconstructed Brain Computed Tomography Images.
Journal of computer assisted tomography
May 26, 2023
OBJECTIVE: The aim of the study is to evaluate whether the prediction of anemia is possible using quantitative analyses of unenhanced cranial computed tomography (CT) with deep learning reconstruction (DLR) compared with conventional methods.
Deep learning application for the classification of Alzheimer's disease using F-flortaucipir (AV-1451) tau positron emission tomography.
Scientific reports
May 19, 2023
The positron emission tomography (PET) with F-flortaucipir can distinguish individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD) from cognitively unimpaired (CU) individuals. This study aimed to evaluate the utility of F-flort...
Identifying the Influencing Factors of Depressive Symptoms among Nurses in China by Machine Learning: A Multicentre Cross-Sectional Study.
Journal of nursing management
May 15, 2023
BACKGROUND: Nurses' high workload can result in depressive symptoms. However, the research has underexplored the internal and external variables, such as organisational support, career identity, and burnout, which may predict depressive symptoms amon...
An AI model to estimate visual acuity based solely on cross-sectional OCT imaging of various diseases.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
May 11, 2023
PURPOSE: To develop an artificial intelligence (AI) model for estimating best-corrected visual acuity (BCVA) using horizontal and vertical optical coherence tomography (OCT) scans of various retinal diseases and examine factors associated with its ac...
Deep Learning-Based Estimation of Implantable Collamer Lens Vault Using Optical Coherence Tomography.
American journal of ophthalmology
May 3, 2023
PURPOSE: To develop and validate a deep learning neural network for automated measurement of implantable collamer lens (ICL) vault using anterior segment optical coherence tomography (AS-OCT).
EE-Explorer: A Multimodal Artificial Intelligence System for Eye Emergency Triage and Primary Diagnosis.
American journal of ophthalmology
May 2, 2023
PURPOSE: To develop a multimodal artificial intelligence (AI) system, EE-Explorer, to triage eye emergencies and assist in primary diagnosis using metadata and ocular images.
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